Re: [Matplotlib-users] [matplotlib-devel] License, freetype

[Joe Kington]

Well, if Freetype were only distributed under the GPL, you couldn't
distribute matplotlib in binary form without providing the source code.

However, Freetype is distributed under more than one license.  (see:
https://www.freetype.org/license.html )

Because it's distributed under a BSD-style license in addition to the GPL,
it can be distributed in binary form, subject to an accreditation clause:
http://git.savannah.gnu.org/cgit/freetype/freetype2.git/tree/docs/FTL.TXT

In the past, I have gotten approval from corporate lawyers at a very large
company to use freetype (and matplotlib) in an application that was being
distributed in binary form.  The dual-licensing of freetype was key in that
particular case.

Or that's my take on it, anyway.  I'm not a Lawyer, so don't consider this
legal advice in any way.
Cheers!
-Joe

On Fri, Feb 17, 2017 at 4:52 PM, Paul Hobson  wrote:

> Chad,
>
> My recollections is that matplotlib doesn't distribute the source code to
> FreeType, it only uses it as a dependency. As such, MPL is in the clear
> with its more permissive licensing.
> -Paul
>
> On Fri, Feb 17, 2017 at 12:45 PM, CAB  wrote:
>
>> Hi, All,
>>
>> I just went to install matplotlib version 2.0.0, and it has a dependency
>> called "freetype". This software appears to be licensed under GPL3.  My
>> reading of that latter license is that, if someone wanted to distribute a
>> compiled version of a program requiring matplotlib, that entire program
>> would fall under the GPL3 license.  I'm sure that would be a non-starter
>> for many, many projects.
>>
>> Does anyone have any takes on this?
>>
>> Chad
>>
>> 
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Re: [Matplotlib-users] Fwd: [matplotlib-devel] RFC: candidates for a new default colormap

[Joe Kington]

On Fri, Jun 5, 2015 at 11:15 AM, Joe Kington joferking...@gmail.com wrote:

 Hopefully I will have some time today to play around with the D option. I
 want to see if I can shift the curve a bit to include more yellows and
 orange so that it can have a mix of cool and warm colors.



 I was thinking the same thing earlier.  Here's my attempt:


Not to jump back on topics too much, but I forgot to attach the colormap to
my earlier e-mail. Here it is.

from matplotlib.colors import LinearSegmentedColormap
from numpy import nan, inf

# Used to reconstruct the colormap in pycam02ucs.cm.viscm
parameters = {'xp': [-6.1859603226509137, -14.579245694353546, -16.377806845432701, -75.430564639197684, 40.576629605406652, 14.197732722912633],
  'yp': [-38.399280575539549, 5.6654676258992822, 11.061151079136692, 51.229016786570725, 35.641486810551555, -4.8261390887290077],
  'min_JK': 27.8125,
  'max_JK': 87.291667}

cm_data = [[ 0.08488181,  0.18308161,  0.67262216],
   [ 0.08312345,  0.19202711,  0.66090297],
   [ 0.08217997,  0.20014429,  0.64976469],
   [ 0.08198433,  0.2075936 ,  0.63911277],
   [ 0.08247594,  0.21447696,  0.62891889],
   [ 0.08359003,  0.2208603 ,  0.61920504],
   [ 0.08524777,  0.2268386 ,  0.60985788],
   [ 0.08738166,  0.23244191,  0.60093206],
   [ 0.08992038,  0.2377215 ,  0.59238917],
   [ 0.09280155,  0.24272587,  0.58416923],
   [ 0.09596381,  0.24747257,  0.57631143],
   [ 0.0993575 ,  0.251998  ,  0.56876406],
   [ 0.10293853,  0.25632645,  0.56150762],
   [ 0.10666336,  0.2604716 ,  0.55456023],
   [ 0.1105036 ,  0.26445857,  0.54787429],
   [ 0.11443019,  0.26830128,  0.54144512],
   [ 0.11841518,  0.27200976,  0.53528253],
   [ 0.12244369,  0.27560103,  0.5293494 ],
   [ 0.12649882,  0.27908535,  0.52363821],
   [ 0.13056226,  0.28246931,  0.51815934],
   [ 0.13462547,  0.28576389,  0.51289008],
   [ 0.13868224,  0.28897881,  0.50780839],
   [ 0.14271871,  0.2921176 ,  0.50293114],
   [ 0.14672864,  0.29518709,  0.49824894],
   [ 0.15071345,  0.29819656,  0.4937278 ],
   [ 0.1546626 ,  0.30114819,  0.48938498],
   [ 0.15857184,  0.30404672,  0.48521597],
   [ 0.16243974,  0.30689736,  0.48120845],
   [ 0.16626692,  0.3097055 ,  0.47734461],
   [ 0.17004651,  0.31247292,  0.47363702],
   [ 0.17377693,  0.31520325,  0.47007969],
   [ 0.1774579 ,  0.31790023,  0.4296],
   [ 0.18109034,  0.32056767,  0.46337356],
   [ 0.18466948,  0.32320706,  0.46022069],
   [ 0.18819439,  0.3258211 ,  0.4571996 ],
   [ 0.19166421,  0.32841234,  0.45430585],
   [ 0.19508052,  0.33098399,  0.4515254 ],
   [ 0.19844012,  0.33353749,  0.44886326],
   [ 0.20174153,  0.33607477,  0.44631801],
   [ 0.20498379,  0.3385979 ,  0.44388601],
   [ 0.20816589,  0.3411088 ,  0.44156374],
   [ 0.21128813,  0.34360991,  0.43934139],
   [ 0.21434794,  0.34610257,  0.43722134],
   [ 0.21734359,  0.34858841,  0.43520227],
   [ 0.22027364,  0.35106916,  0.433281  ],
   [ 0.22313653,  0.35354653,  0.43145439],
   [ 0.22593054,  0.3560222 ,  0.42971931],
   [ 0.22865443,  0.35849817,  0.42806853],
   [ 0.23130553,  0.36097577,  0.42650261],
   [ 0.23388157,  0.36345656,  0.42501923],
   [ 0.23638033,  0.36594216,  0.42361498],
   [ 0.23879948,  0.36843418,  0.42228628],
   [ 0.24113658,  0.37093422,  0.42102943],
   [ 0.24338915,  0.37344386,  0.41984053],
   [ 0.24555463,  0.37596471,  0.41871526],
   [ 0.2476304 ,  0.37849855,  0.41764727],
   [ 0.24961385,  0.3810466 ,  0.41663458],
   [ 0.25150254,  0.38361032,  0.41567235],
   [ 0.25329409,  0.38619113,  0.41475548],
   [ 0.25498629,  0.38879034,  0.41387864],
   [ 0.25657711,  0.39140923,  0.41303625],
   [ 0.25806477,  0.39404893,  0.4153],
   [ 0.25944772,  0.3967105 ,  0.41143149],
   [ 0.26072475,  0.39939485,  0.41065702],
   [ 0.26189499,  0.40210277,  0.40989287],
   [ 0.26295789,  0.4048349 ,  0.40913271],
   [ 0.26391332,  0.40759175,  0.40837023],
   [ 0.2647615 ,  0.41037366,  0.40759912],
   [ 0.26550293,  0.41318086,  0.40681296],
   [ 0.2661385 ,  0.41601343,  0.40600552],
   [ 0.2972,  0.41887118,  0.40517133],
   [ 0.26709825,  0.42175387,  0.40430471],
   [ 0.26742604,  0.42466112,  0.40340023],
   [ 0.26765527,  0.42759246,  0.40245273],
   [ 0.26778838,  0.43054729,  0.40145736],
   [ 0.26782797,  0.43352494,  0.40040961],
   [ 0.2677768 ,  0.43652465,  0.39930525],
   [ 0.26763777,  0.4395456 ,  0.3981404 ],
   [ 0.26741386,  0.44258692,  0.39691145],
   [ 0.26710811,  0.4456477 ,  0.39561511],
   [ 0.26672365,  0.448727  ,  0.39424836],
   [ 0.26626363,  0.45182384,  0.39280844],
   [ 0.26573088,  0.45493737,  0.39129233],
   [ 0.26512858,  0.4580666 ,  0.38969761

Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap

[Joe Kington]

Not to plug one of my own answers to much, but here's a basic example.
http://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib

I've been meeting to submit a PR with a more full featured version for a
few years now, but haven't.
On Jun 5, 2015 4:45 PM, Sourish Basu sourish.b...@gmail.com wrote:

  On 06/05/2015 01:20 PM, Eric Firing wrote:


 Reminder: in matplotlib, color mapping is done with the combination of a
 colormap and a norm.  This allows one to design a norm to handle the
 mapping, including any nonlinearity or difference between the handling
 of positive and negative values.  This is more general than customizing
 a colormap; once you have a norm to suit your purpose, you can use it
 with any colormap.

 Maybe this is actually what you are already doing, but I wanted to point
 it out here in case some readers are not familiar with this
 colormap+norm strategy.


 Actually, I didn't use norms because I never quite figured out how to use
 them or how to make my own. If there's a way to create a norm with a custom
 mid-point, I'd love to know/use that.

 -Sourish



 Eric

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Re: [Matplotlib-users] Confused about rgb_to_hsv and hsv_to_rgb

[Joe Kington]

I think you're asking how to blend a custom intensity image with an rgb
image. (I'm traveling and just have my phone, so you'll have to excuse my
lack of examples.)

There are several ways to do this. Basically, it's analogous to blend
modes in Photoshop etc.

Have a look at the matplotlib.colors.LightSource.blend_overlay and
blend_soft_light functions in the current github head. (And also
http://matplotlib.org/devdocs/examples/specialty_plots/topographic_hillshading.html
)

If you're working with 1.4.x, though, you won't have those functions.

However, the math is very simple. Have a look at the code in those
functions in the github head. It's basically a one liner.

You'll need both the 4-band rgba image and the 1 band intensity/hillshade
image to be floating point arrays scaled from 0-1. However, this is the
default in matplotlib.

How that helps a bit, and sorry again for the lack of examples!
Joe
OK, I understand.


Could you suggest a way to reduce that 3D array to a 2D array and plot it
with a specific colormap, while preserving the shading?

I did something similar in Matlab

https://mycarta.wordpress.com/2012/04/05/visualization-tips-for-geoscientists-matlab-part-ii/

But it took using some custom functions and a ton of asking and tinkering,
and I'm not quite at that level with matplotlib, so any suggestion would
be appreciated

Thanks,
Matteo

On Thu, May 21, 2015 4:10 pm, Eric Firing wrote:


 Colormapping occurs only when you give imshow a 2-D array of numbers to
 be mapped; when you feed it a 3-D array of RGB values, it simply shows
 those colors.  For colormapping to occur, it must be done on a 2-D array
 as a step leading up to the generation of your img_array.

 Eric

 On 2015/05/21 5:50 AM, Matteo Niccoli wrote:

 I posted a question on stackoverflow about creating with making my own
 shading effect (I want to use horizontal gradient for the shading).
 http://stackoverflow.com/questions/30310002/issue-creating-map-shading-
 in-matplotlib-imshow-by-setting-opacity-to-data-gradi


 Unfortunately I cannot share the data because I am using it for a
 manuscripts, but my notebook with full code listing and plots, here:
 http://nbviewer.ipython.org/urls/dl.dropbox.com/s/2pfhla9rn66lsbv/surfa
 ce_shading.ipynb/%3Fdl%3D0

 The shading using gradient is implemented in two ways as suggested in
 the answer. What I do not understand is why the last plot comes out with
 a rainbow-like colors, when I did specify cubehelix as colormap.

 hsv = cl.rgb_to_hsv(img_array[:, :, :3]) hsv[:, :, 2] = tdx_n
 rgb = cl.hsv_to_rgb(hsv) plt.imshow(rgb[4:-3,4:-3], cmap='cubehelix')
 plt.show()


 Am I doing something wrong or is this unexpected behavior; is there a
 workaround?


 Thanks
 Matteo




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Re: [Matplotlib-users] Key events using nbagg backend

[Joe Kington]

Woops! You're absolutely right! I was completely confused!

I mixed up the new nbagg backend with the way ipython notebooks used to
display matplotlib figures.  The nbagg backend is indeed interactive (and I
have no idea why key press callbacks aren't supported, then).

This is what happens when I reply to e-mail without putting much thought
into it.

Thanks!
-Joe

On Thu, Dec 4, 2014 at 8:58 PM, Brendan Barnwell brenb...@brenbarn.net
wrote:

 On 2014-12-04 15:40, Joe Kington wrote:
  Nbagg is non-interactive, similar to Agg. No events other than draw
 events
  are supported, as far as I know.

 If that's the case, the release notes should probably make that
 clear.
   Right now at
 http://matplotlib.org/users/whats_new.html#the-nbagg-backend it says:
 Phil Elson added a new backend, named “nbagg”, which enables
 interactive figures in a live IPython notebook session.  The word
 interactive certainly could lead people to believe that the backend
 is, in fact, interactive.

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Re: [Matplotlib-users] Key events using nbagg backend

[Joe Kington]

Nbagg is non-interactive, similar to Agg. No events other than draw events
are supported, as far as I know.

I think there are long term plans to change that (eg, webagg and
mplh5canvas), but it's a fairly tricky problem.

How that helps clarify why things aren't working, anyway.
-Joe
On Dec 4, 2014 11:04 AM, reem17 el...@u.northwestern.edu wrote:

 I'm using matplotlib version 1.4.2 on linux and am having trouble getting
 key
 event
 callbacks when using the nbagg backend in an ipython notebook (i've tried
 in
 firefox and chromium).
 The key press events seem to not be processed (no matter what key I try to
 press).  This code does work with the qt backend.

 %matplotlib nbagg
 import matplotlib.pyplot as plt
 f=plt.figure()
 plt.plot([1,2,3,4])
 def handler(event):
 plt.plot([4,3,2,1])
 f.canvas.mpl_connect('key_press_event',handler);


 Thanks for any help.



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Re: [Matplotlib-users] Matplotlib-users Digest, Vol 102, Issue 39

[Joe Kington]

 On Fri, Nov 21, 2014 at 10:42 AM, Pedro Marcal pedrovmar...@gmail.com
 wrote:
 @MariaLukis, I had to go through contortions to plot an arbitrary
 quadrilateral mesh, in 3D. I resolved it by storing every line plotted and
 retracing the best set to take me to the starting point of the quad I was
 plotting. It would have been much easier if I had the function of lifting
 my pen and move while not plotting. But then I did not know how to get
 intpo matplotlib to perform what is a simple mod.

...snip...



First off, ``pcolormesh`` will happily plot arbitrary quadrilateral meshes,
so long as you can describe the points in a regular manner.  For example:

import matplotlib.pyplot as plt
import numpy as np

shape = (10, 10)
y, x = np.mgrid[:shape[0], :shape[1]]
# Distort the grid so that it's no longer regular
x = x + np.random.normal(0, 0.2, shape)
y = y + np.random.normal(0, 0.2, shape)

z = np.random.random(shape)

fig, ax = plt.subplots()
ax.pcolormesh(x, y, z)
plt.show()


​Also, lifting the pen while not plotting is the basis of how paths are
handled in matplotlib.  Normally, you wouldn't drop down to this
lower-level API very often, but it underpins a lot of  higher-level
matplotlib artists.  For example, let's draw 4 squares with one path:

import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch

codes = Path.LINETO * np.ones(5, dtype=np.uint8)
codes[0] = Path.MOVETO

x = np.array([0, 1, 1, 0, 0])
y = np.array([0, 0, 1, 1, 0])

numsquares = 4
x = np.hstack([x + 2*i for i in range(numsquares)])
y = np.hstack([y + 2*i for i in range(numsquares)])
codes = np.hstack(numsquares * [codes])

path = Path(np.c_[x, y], codes)
patch = PathPatch(path, facecolor='red')

fig, ax = plt.subplots()
ax.add_patch(patch)
ax.autoscale()

ax.axis('equal')
ax.margins(0.05)
plt.show()


​Hopefully those examples help a bit (or at least give food for thought).
Cheers,
-Joe
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Re: [Matplotlib-users] Basemap from proj4 string

[Joe Kington]

I agree that it would be useful, but basemap doesn't support EPSG codes, so
this isn't fully possible in general.

On a side note, if you're ever just wanting to transform coordinates,
pyproj supports generic proj4 strings.  (e.g. proj =
pyproj.Proj(your_proj4_string))

It might be possible to initialize a projection in Cartopy from a generic
pyproj projection... I'm not sure, though.

At any rate, that doesn't answer your question at all, but consider it food
for thought.
Cheers,
-Joe

On Fri, Nov 7, 2014 at 7:16 AM, Knut-Frode Dagestad 
knutfrodesop...@hotmail.com wrote:

  As an example, say I have a proj4 string defining a spatial reference
 system:
 p4 = '+lon_0=0.0 +lat_ts=60.0 +R=6370997.0 +proj=stere +x_0=-0.0 +units=m
 +y_0=4327039.11059 +lat_0=90.0'

 Then one can construct a Basemap from this as:
 map = Basemap(projection='stere', lon_0=0., lat_0=90, lat_ts=60,
 llcrnrlat=50, urcrnrlat=70, llcrnrlon=0, urcrnrlon=20)


 I could make a function to construct Basemap arguments from any user
 specified proj4 string (which is out of control of my application). But
 that function would probably be long and contain a lot of
 projection-specific if-then-switches. Copying proj4 arguments directly does
 not work, e.g. if passing x_0=0 Basemap complains that x_0 is an
 unexpected argument.
 The problem of making a long parser is not that this is tedious, but that
 it is error prone and unstable e.g. with respect to future upgrades of
 Basemap or proj4 libraries.

 So as Basemap obviously does have an algorithm to create a proj4 string
 from its input arguments, I was hoping the reverse algorithm would also be
 available somehow. E.g. it would be very useful to be able to initialise a
 Basemap with:

 map = Basemap(proj4=p4, llcrnrlat=50, urcrnrlat=70, llcrnrlon=0,
 urcrnrlon=20)

 Knut-Frode


 --
 Date: Wed, 5 Nov 2014 09:32:16 -0800


 I think some examples would help. Which proj4 string are you looking to
 use? How are you currently constructing the map?
 -p

 On Wed, Nov 5, 2014 at 7:53 AM, knutfrode knutfrodesop...@hotmail.com
 wrote:

 Does anyone know if this is possible?





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Re: [Matplotlib-users] Inverting a datetime / plot_date y-axis

[Joe Kington]

A quick way to do this is ``ax.invert_yaxis()``  (and invert_xaxis() for
the x-axis).  That way you preserve auto-scaling and don't wind up with
manually set axis limits.

What you did should have worked, but ``ymin`` and ``ymax`` are probably
datetime objects.  ``ylim`` isn't smart enough to convert them to the
datetime units that matplotlib uses internally.

Hope that helps!
-Joe

On Tue, Sep 16, 2014 at 4:00 AM, Gerd Wellenreuther 
gerd.wellenreut...@xfel.eu wrote:

 Dear all,

 I hope some of you could help me out. I am currently trying to generate
 some timetables using matplotlib.pyplot.plot_date, having the time-axis
 on the y-axis. Typically, one would like to read these plots from top to
 bottom, from older to newer items (future on the bottom). Unfortunately,
 the default enumeration of the y-axis is the other way around, and it
 resists my attempts to invert its direction e.g. by changing limits
 using matplotlib.pyplot.ylim (Traceback below). I found a quite old
 entry in stackoverflow which is most probably outdated, at least the
 proposed solution did not work for me...
 (
 http://stackoverflow.com/questions/5804969/displaying-an-inverted-vertical-date-axis
 ).

 Since the longish traceback seems to try to tell me I did not understand
 some kind of important - even trivial - point about those datetime-axis
 maybe some of you came about this problem before?

 Thanks, Gerd

 P.S.: Traceback after trying to use something like
 matplotlib.pyplot.ylim(ymax,ymin):
  Traceback (most recent call last):
File C:\Users\gwellenr\Desktop\Test_Sabine\Plot_csv.py, line 187,
  in module
  matplotlib.pyplot.savefig(save_path+'test.png')
File C:\Python27\lib\site-packages\matplotlib\pyplot.py, line 561,
  in savefig
  return fig.savefig(*args, **kwargs)
File C:\Python27\lib\site-packages\matplotlib\figure.py, line
  1421, in savefig
  self.canvas.print_figure(*args, **kwargs)
File C:\Python27\lib\site-packages\matplotlib\backend_bases.py,
  line 2220, in print_figure
  **kwargs)
File
  C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py,
  line 505, in print_png
  FigureCanvasAgg.draw(self)
File
  C:\Python27\lib\site-packages\matplotlib\backends\backend_agg.py,
  line 451, in draw
  self.figure.draw(self.renderer)
File C:\Python27\lib\site-packages\matplotlib\artist.py, line 55,
  in draw_wrapper
  draw(artist, renderer, *args, **kwargs)
File C:\Python27\lib\site-packages\matplotlib\figure.py, line
  1034, in draw
  func(*args)
File C:\Python27\lib\site-packages\matplotlib\artist.py, line 55,
  in draw_wrapper
  draw(artist, renderer, *args, **kwargs)
File C:\Python27\lib\site-packages\matplotlib\axes.py, line 2086,
  in draw
  a.draw(renderer)
File C:\Python27\lib\site-packages\matplotlib\artist.py, line 55,
  in draw_wrapper
  draw(artist, renderer, *args, **kwargs)
File C:\Python27\lib\site-packages\matplotlib\axis.py, line 1091,
  in draw
  ticks_to_draw = self._update_ticks(renderer)
File C:\Python27\lib\site-packages\matplotlib\axis.py, line 945,
  in _update_ticks
  tick_tups = [t for t in self.iter_ticks()]
File C:\Python27\lib\site-packages\matplotlib\axis.py, line 889,
  in iter_ticks
  majorLocs = self.major.locator()
File C:\Python27\lib\site-packages\matplotlib\dates.py, line 802,
  in __call__
  self.refresh()
File C:\Python27\lib\site-packages\matplotlib\dates.py, line 820,
  in refresh
  self._locator = self.get_locator(dmin, dmax)
File C:\Python27\lib\site-packages\matplotlib\dates.py, line 896,
  in get_locator
  raise ValueError('No sensible date limit could be found in the '
  ValueError: No sensible date limit could be found in the AutoDateLocator.



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Re: [Matplotlib-users] Region within contour -- 2D array

[Joe Kington]

Why not just use boolean indexing?

E.g. to find the region that falls between 5 and 10, do (z =5)  (z =
10):

In [1]: import numpy as np

In [2]: x, y = np.mgrid[-10:10, -10:10]

In [3]: z = np.hypot(x, y)

In [4]: result = (z = 5)  (z = 10)

In [5]: result.astype(int)
Out[5]:
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
   [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
   [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
   [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
   [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
   [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
   [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
   [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
   [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
   [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]])

Cheers,
-Joe



On Thu, Aug 28, 2014 at 8:23 PM, Eric Firing efir...@hawaii.edu wrote:

 On 2014/08/28, 3:02 AM, Matthew Czesarski wrote:
  Hi Matplotlib Users!
 
 
 
  I have some 2-d arrays, which i am displaying  with implot, and deriving
  contours for with contour.  Easy -  I'm just pulling them out of
  collections[0].get_paths() .
 
  However what's not easy is that I would like to recover a 1-0 or
  True-False array of the array values (pixels) that fall within the
  contours. Some line crossing algorithm/floodfill could do it, but I
  guess that matplotlib's fill() or contourf() must do this under the hood
  anyway. I've looked into the output both functions, but I don't see
  anything obvious..
 
  Does anybody know if there's an a way to pull out a such an array from
  matplotlib?   Any pointers are appreciated!

 Make an array of (x, y) pairs from the X and Y you use in your call to
 contour, and then feed that array to the contains_points() method of
 your contour Path.  This will give you the desired Boolean array for any
 given Path; depending on what you want, you might need to combine arrays
 for more than one Path.

 To get closed paths, I think you will want to use contourf, not contour.

 Eric



 
  Cheers,
  Matt
 
 
 
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Re: [Matplotlib-users] pick_event after zooming/panning stops working.

[Joe Kington]

I think the OP's desire is to have pick events fire after the zoom has been
triggered.

Currently, after you zoom (or pan), the zoom tool is still active until you
click it again.  Pick events won't fire while the zoom tool is the selected
tool, and you have to manually de-select it (i.e. click the zoom button
again for pick events to work).

The current behavior is the right default choice, i.m.o., but it's
counter-intuitive when combined with pick events.

When you're building a gui to interact with data (or, for example, when
using mpldatacursor -- this is a question I get a lot), a common
expectation is that after you zoom once, the zoom tool is no longer
active.  Pick events should work again.

Currently, you have to subclass (or monkey-patch) the toolbar to make this
happen.  It's a bit of a pain.  (It's more complicated that just setting
`fig.canvas.toolbar._active`.)  (If I'm wrong about that, please let me
know!!!)

It would be nice to have an easier way to deactivate the zoom/pan tool.
I think the new toolbar might have that, but I haven't checked.

Cheers,
-Joe


On Thu, Aug 21, 2014 at 1:41 PM, Benjamin Root ben.r...@ou.edu wrote:

 Imagine someone creates some event that would modify an artist upon
 picking, or do some expensive calculation, or some other action. But, I
 seriously doubt anybody would want those actions to fire while using the
 zoom/pan tool. Especially since the mouse cursor looks totally different. I
 am curious why you would expect pick events to fire while using pan/zoom.
 What is the user-story that compels that expectation? Perhaps I could be
 convinced otherwise to offer some sort of toggle.



 On Thu, Aug 21, 2014 at 2:33 PM, Michael Kaufman kaufma...@ornl.gov
 wrote:

 What kind of bad stuff happens if we were to allow that?

 M


 On 8/21/14 2:29 PM, Benjamin Root wrote:

 Yes, those tools do snarf up pick events via the widgetlock mechanism,
 IIRC. This is entirely intentional, and I an not sure there is a bug
 here to fix.


 On Thu, Aug 21, 2014 at 12:01 PM, Thomas Caswell tcasw...@gmail.com
 mailto:tcasw...@gmail.com wrote:

 On Thu, Aug 21, 2014 at 9:44 AM, Michael Kaufman kaufma...@ornl.gov
 mailto:kaufma...@ornl.gov wrote:
  
  # plot axvlines here... etc.
  
  global cids
  
  # remove any previous connections
  for i in cids:
 gcf().canvas.mpl_disconnect(i)
  cids = []
  
  cids.append(gcf().canvas.mpl_connect('pick_event',self.pick))
  
 cids.append(gcf().canvas.mpl_connect('button_press_event',
 self.click))
  
  draw()
  
   def pick(self, event):
  thisline = event.artist
  xdata, ydata = thisline.get_data()
  print xdata[0]
  
   def click(self, event):
  print clicked


 See this minimal example

 ```
 import matplotlib.pyplot as plt
 fig, ax = plt.subplots()

 ax.axvline(.5, picker=6)
 ax.plot(range(3))
 cids = []

 plt.draw()

 def pick(event):
 thisline = event.artist
 xdata, ydata = thisline.get_data()
 print xdata[0]

 def click(event):
 print clicked


 cids.append(fig.canvas.mpl_connect('pick_event', pick))
 cids.append(fig.canvas.mpl_connect('button_press_event', click))

 ```

 If you turn the zoom/pan tool off the picker works again.  I suspect
 that there is some logic underneath those tools that are snarfing
 events when the are turned on to avoid messy conflicts.  There is
 some
 work going on (MEP22 iirc) to update the toolbar and make our tool
 handling saner.

 Tom
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Re: [Matplotlib-users] Example showing differences between Mac O$ and Windows use

[Joe Kington]

That should be `matplotlib.use('TkAgg')`, not Agg. Agg is a
non-interactive backend, while TkAgg is an interactive Tkinter wrapper
around the Agg backend.


On Thu, Mar 13, 2014 at 9:53 AM, Jeroen Hegeman jeroen.hege...@gmail.comwrote:

 Hi Christophe,

 This is (I think) a known limitation of the OS X backend. One way around
 this is to use another backend. Which backends are available depends on how
 your matplotlib was built. (And unfortunately I don't know how to figure
 out which ones are available, apart from trying.) In my case (matplotlib
 from homebrew on OS X 10.9.2) the TkAgg backend works.

 To use the TkAgg backend insert these two lines:
  import matplotlib
  matplotlib.use('Agg')
 before the line
  import matplotlib.pyplot as plt

 I hope that helps.

 Best regards,
 Jeroen


 On 13 Mar 2014, at 15:33, Christophe Bal projet...@gmail.com wrote:

  Hello,
  I'm a little disappointed by the following test program coming from this
 post.
 
  What are the technical reasons that make fail the following code under
 Mac O$ ?
 
  Best regards.
  Christophe BAL
 
   TEST ---
 
  from random import randint,
  choice
 
  import
  time
 
  import matplotlib.pyplot as
  plt
 
  import matplotlib.patches as
  mpatches
 
  back_color
  = black
 
  colors
  = ['red', 'green', 'cyan', 'yellow']
 
  width
  , height = 16, 16
 
 
  fig
  , ax = plt.subplots()
 
  ax
  .set(xlim=[0, width], ylim=[0, height]) # Or use ax.axis([x0,x1,y0,y1])
 
 
 
  # Be sure to draw the canvas once before we start blitting. Otherwise
  # a) the renderer doesn't exist yet, and b) there's noting to blit onto
 
  fig
  .canvas.draw()
 
 
 
  def update():
 
 x
  = randint(0, width - 1)
 
 y
  = randint(0, height - 1)
 
 
 rect
  = mpatches.Rectangle(
 
 
  (x, y), 1, 1,
 
 facecolor
  = choice(colors),
 
 edgecolor
  =
  back_color
 
  )
 
 ax
  .add_artist(rect)
 
 
 start
  = time.time()
 
 ax
  .draw_artist(rect)
 
 fig
  .canvas.blit(ax.bbox)
 
 
  print(draw , time.time() - start)
 
 
  timer
  = fig.canvas.new_timer(interval=1)
 
  timer
  .add_callback(update)
 
  timer
  .start()
 
 
  plt
  .show()
 
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Re: [Matplotlib-users] multiplot in a for loop

[Joe Kington]

On Mar 10, 2014 1:00 PM, Gabriele Brambilla 
gb.gabrielebrambi...@gmail.com wrote:

 And how can I select in which position of the grid of plots put my
errorbar plot?

 like axes[i].errorbar(...)?

Exactly. Except that axes will be a 2d array if you have multiple rows and
columns, so it would be axes[i,j].errorbar(...).

If you just want to iterate over all of the subplots, use for ax in
axes.flat:.

Hope that helps,
-Joe

 thanks

 Gabriele


 2014-03-10 13:51 GMT-04:00 Paul Hobson pmhob...@gmail.com:

 So it sounds like you're not specifying the subplots correctly.

 I recommend using `fig, axes = plt.subplots(nrows=R, ncols=C)` as a
replacement for `fig = plt.figure` and `fig.add_subplot()`.

 `axes` will be a numpy array of Axes objects through which you'll be
able to loop along with your other variable.

 Good luck.
 -paul


 On Mon, Mar 10, 2014 at 10:46 AM, Gabriele Brambilla 
gb.gabrielebrambi...@gmail.com wrote:

 No, if you look better near the zero there are some COLOURED lines: you
have this impression because the values in EcutS are enormous respect the
other one in GAMMAs and Bees. When you plot them all together the other
ones disappear... I don't want to plot them all together.

 Excuse me but it's very difficult for me to give you a self running
code: it's an enormous code with a lot of things that recall each others...

 Thanks

 Gabriele


 2014-03-10 12:54 GMT-04:00 Paul Hobson pmhob...@gmail.com:

 Gabriele,

 I'm confused. I only see 1 series in each subplot. Could you trim your
example down into some code that we can copy, paste, and run? A more
thorough description of the problem might help too.
 -p


 On Mon, Mar 10, 2014 at 9:39 AM, Gabriele Brambilla 
gb.gabrielebrambi...@gmail.com wrote:

 Hi I'm trying to do a particular plot similar to this one:
http://matplotlib.org/examples/pylab_examples/line_styles.html

 but my code pastes different datas in the same plot. I want only one
array per each plot. I attach the wrong result

 fdatas = plt.figure()
 for filename in filenames:

 [various things]

 for obsangl in angles:
   [various things]

   #fitspectrum4.fitSpec is one my
personal fitting routine

   pa0, era0, pa1, era1, pa2, era2, pa3,
era3 = fitspectrum4.fitSpec(eels, averspe, (1.0)*(10**(-8)), 0.66, 10**6, 1)
   GAMMAs.append(pa1)
   EcutS.append(pa2)
   Bees.append(pa3)
   GeR.append(era1)
   EeR.append(era2)
   beR.append(era3)

   ialph += 1
   ialpg = ialph
   axum1 = fdatas.add_subplot(numalp, 3, ialpg)
   da1 = axum1.errorbar(angles, GAMMAs, yerr=GeR, marker =
.)
   ialpg = ialph + 1
   axum2 = fdatas.add_subplot(numalp, 3, ialpg)
   da2 = axum2.errorbar(angles, EcutS, yerr=EeR, marker =
.)
   ialpg = ialph + 2
   axum3 = fdatas.add_subplot(numalp, 3, ialpg)
   da3 = axum3.errorbar(angles, Bees, yerr=beR, marker =
.)


 thanks

 Gabriele


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[Matplotlib-users] OT: Just because you can...

[Joe Kington]

There's a bad meme here.  Hope you'll forgive the distraction!

import matplotlib.pyplot as plt
import numpy as np

def main():
t = np.linspace(0, 4*np.pi, 1000)
fig, ax = plt.subplots()
ax.plot(t, np.cos(t))
ax.plot(t, np.sin(t))

for _ in range(10):
fig = inception(fig)

plt.show()

def fig2rgb_array(fig):
fig.canvas.draw()
buf = fig.canvas.tostring_rgb()
ncols, nrows = fig.canvas.get_width_height()
return np.fromstring(buf, dtype=np.uint8).reshape(nrows, ncols, 3)

def inception(fig):
fig.tight_layout()
fig.set_facecolor('white')
data = fig2rgb_array(fig)
fig.clf()
fig.add_subplot(111).imshow(data)
return fig

main()


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Re: [Matplotlib-users] Most generic way to wrap collections

[Joe Kington]

On Tue, Jan 7, 2014 at 2:29 PM, Adam Hughes hughesada...@gmail.com wrote:

 Sorry, quick followup.  I did find the gallery example to plot multiple
 patches together:

 http://matplotlib.org/examples/api/patch_collection.html

 That's excellent.  Now I guess my question is how best to generalize the
 process of turning my objects into patches.  I think I will just try to
 keep the geometry (ie line -- mpatch.Line) unless anyone has any better
 suggestions.


As you've already found out, it sounds like you want a PatchCollection.

There is one catch, though.  Your lines/curves will need to be converted to
PathPatches (which is trivial), which can then have a facecolor.  Because
all items in a collection will have the same facecolor by default, this
means that your lines will become filled polygons, unless you specify
otherwise.

Therefore, you'll need to do something like this:

import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection

# Just a simple line, but it could be a bezier curve, etc.
line = Path([(-20, -20), (-10, 10), (20, 20)])

# We'll need to convert the line to a PathPatch, and we'll throw in a
circle, too
line = mpatches.PathPatch(line)
circle = mpatches.Circle([0, 0])

# If we don't specify facecolor='none' for the line, it will be filled!
col = PatchCollection([line, circle], facecolors=['none', 'red'])

fig, ax = plt.subplots()
ax.add_collection(col)
ax.autoscale()
plt.show()

Alternatively, you can just put the lines/curves in a PathCollection and
the patches/polygons/etc in a PatchCollection.

Hope that helps!
-Joe



 Thanks!


 On Tue, Jan 7, 2014 at 3:08 PM, Adam Hughes hughesada...@gmail.comwrote:

 Hi,

 I am working on a library for image analysis which stores particles as
 indexed numpy arrays and provides functionality for managing the particles
 beyond merely image masking or altering the arrays directly.  I've already
 designed classes for many common shapes including Lines/Curves,
 Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
 overlap).

 What I'd really LOVE to do would be able to generate a
 matplotlib.Collection instance from these objects as generally as possible.
  Then, I'd be able to show data as a masked image, but also get a really
 nice looking plot from the objects in their Collection representation.

 So my question really is in the implementation.  First, is there a
 general collection object that could work with ANY shape, or am I better
 off matching my shape to that collection?  For example:

 line -- LineCollection   *vs.*line -- GeneralCollection
 circle -- CircleCollection   circle --- GeneralCollection

 And then, is the Collections plotting API flexible enough to mix all of
 these types together?  Or would I have to settle for only being able to
 plot a collection of any 1 shape type at at time?

 I will delve into the API further, but ascertaining this information
 would really help me get started.

 Thanks




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Re: [Matplotlib-users] Plotting with more than two y-axes (with twinx?)

[Joe Kington]

Hi Daniel,

For what it's worth, the code runs perfectly for me as-is on matplotlib
1.3.1 with python 2.7 on linux.

However, based on your description, I'd guess that the second call to
`twinx` is returning the same axes object.

What happens when you do:

print id(axes[1]), id(axes[2])

Are the id numbers the same or different?

If they're the same, there may have been a regression/change that causes
`twinx` to return the same object instead of creating a new axes.

Cheers!
-Joe



On Wed, Nov 27, 2013 at 5:08 PM, dodermat dani.oderm...@gmail.com wrote:

 Dear all

 What I want to accomplish was produced two years ago in  a stackoverflow
 snippet by Joe Kington
 
 http://stackoverflow.com/questions/7733693/matplotlib-overlay-plots-with-different-scales
 
 , and shown in the first figure below. However, when I use his snippet in
 matplotlib 1.3.x, I get an output where the third axis replaces the second
 axis, and the blue dots are accordingly distributed in only the lower half
 of the plot (see second figure below). I considered downdating to an older
 version of matplotlib, but then I came across  a remark in the matplotlib
 FAQ http://matplotlib.org/faq/howto_faq.html#multiple-y-axis-scales  .
 According to this remark, such a feature for twinx is on the wish list and
 thus not very likely to be available in an older version.

 Can someone please explain the Kington magic to me?


 http://matplotlib.1069221.n5.nabble.com/file/n42556/ksRXk.png
 http://matplotlib.1069221.n5.nabble.com/file/n42556/figure_1.png



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[Matplotlib-users] Is there anything pcolor can do that pcolormesh can't?

[Joe Kington]

Until very 
recentlyhttp://stackoverflow.com/questions/20069545/2d-plotting-with-colors-like-spectrograms-magnitude-plots#comment29914445_20069545,
I had somehow wound up with the impression that `pcolormesh` only handles
rectilinear coordinate arrays, while `pcolor` can handle arbitrarily shaped
quadrilaterals.

Apparently, both pcolor and pcolormesh can handle any type of
quadrilateral. For example:

import numpy as np
import matplotlib.pyplot as plt

y, x = np.mgrid[:10, :10].astype(float)
x += 0.5 * np.random.random(x.shape)
y += 0.5 * np.random.random(y.shape)
z = np.random.random(x.shape)

fig, axes = plt.subplots(ncols=2)
axes[0].pcolor(x, y, z)
axes[1].pcolormesh(x, y, z)
plt.show()


Given that, is there any effective difference between `pcolor` and
`pcolormesh` other than that `pcolor` creates a `PolyCollection` and
`pcolormesh` creates a `QuadMesh`?

In other words, is there anything that `pcolor` can display that
`pcolormesh` can't?

Thanks!
-Joe
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Re: [Matplotlib-users] indicating directions on stereographic projection.

[Joe Kington]

Sourav - Are you by chance trying to make a stereonet?  If so, your
question makes a bit more sense.  If that's what you're doing, have a look
at mplstereonet. https://github.com/joferkington/mplstereonet It currently
doesn't support polar stereonets, but that's something I've been meaning to
add for a long time.

If that's not what you're doing, then perhaps you wanted something similar
to this? (The directions don't have much meaning, as others have already
pointed out):

from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt

m = Basemap(projection='npstere',boundinglat=10,lon_0=270,resolution='l')
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
m.drawmapboundary(fill_color='aqua')

# draw parallels and meridians.
m.drawparallels(np.arange(-80.,80.,20.))
ticks = m.drawmeridians(np.arange(-180.,180.,20.),
labels=[True, True, True, True])

# Change the meridian label at 0 and 180 to East and West
for value, label in zip([0.0, -180.0], ['East', 'West']):
ticks[value][1][0].set_text(label)

plt.show()

Hope that helps!
-Joe



On Wed, Nov 6, 2013 at 1:34 PM, Jason Grout jason-s...@creativetrax.comwrote:

 On 11/6/13 1:10 PM, Paul Hobson wrote:
  I am **very far** from a GIS expert, but I believe that the cardinal
  directions are ambiguous at the poles. In other words, if you're
  standing on the North Pole, it'd difficult to head in any direction
  that's not towards the south pole.
 
  Curious to hear if I'm wrong, though.

 I'm sure I'm even farther from being a GIS expert (and probably farther
 from one as well :), but I think you raise an interesting point.  My
 compass would give me directions at the geographic north pole, given
 that the magnetic north pole is different from the geographic north
 pole.  I'm curious how valid it would be to use magnetic north to assign
 directions at the geographic north pole.  My guess is not very valid,
 but kind of cool nonetheless.

 Jason



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Re: [Matplotlib-users] pick event for images

[Joe Kington]

Hi Nils,


I'm glad you found it useful!

I'm certainly not opposed to seeing it integrated into matplotlib in the
long run.  At the moment, though, it's probably changing a bit too
quickly.  There's a lot of little things I'd like to add to it.

On the other hand, if it ever were integrated upstream, there's an
opportunity to clean things up quite a bit, as mpldatacursor currently has
quite a few workarounds for minor bugs in old versions of mpl and minor api
changes, etc.

Cheers!
-Joe


On Thu, Oct 31, 2013 at 8:00 AM, Nils Wagner nils...@googlemail.com wrote:

 Hi Joe,

 Just now I installed your package mpldatacursor and run the example
 image_example.py.
 Awesome !
 Is there a chance to integrate it into matplotlib ?

 Nils



 On Wed, Oct 30, 2013 at 5:58 PM, Joe Kington joferking...@gmail.comwrote:


 On Oct 30, 2013 9:43 AM, Nils Wagner nils...@googlemail.com wrote:
 
  Hi all,
 
  How can I retrieve the corresponding color value in percent, when I
 click on the image ?
 

 You have to jump through a couple of hoops.

 Have a look at the _coords2index function in pick_info.py for
 mpldatacursor.
 https://github.com/joferkington/mpldatacursor/blob/master/mpldatacursor/pick_info.py

 Also, mpldatacursor might be useful for what you're doing. Not to plug my
 own project too much, but it does exactly this, among other things.

 Hope that helps!
 -Joe

  fig = plt.figure()
  ax  = fig.add_subplot(111)
  cax = ax.imshow(col[:,::2],
 interpolation='nearest',extent=[0.5,20.5,0.5,2
  0.5],alpha=1,picker=5)
 
  fig.canvas.mpl_connect('pick_event', onpick)
 
  def onpick(event):
   ...
 
 
  Nils
 
 
 
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Re: [Matplotlib-users] pick event for images

[Joe Kington]

Hi Daniele,

First off, the FSF uses and endorses a number of non GPL licenses.  They
reccomend the Apache license over MIT-style licenses for permissive cases
due to patent issues, but they don't require it.  In fact, even the GNU
project has several X11 (the FSF's name for the MIT license) licensed
projects (e.g. Off the top of my head, I know ncurses is both an official
GNU project and is X11-licensed.).

However, I'm glad you brought this up.  I haven't actually filled out a
copyright transfer form to the FSF.  I intended to at one point, but never
did and didn't think about the copyright assignment in the license header
very much. Not doing so, and leaving copyright in the name of any potential
contributors actually makes things quite a bit simpler.

At any rate, it's certainly something I should clarify.

Thanks!
-Joe


On Sat, Nov 2, 2013 at 11:55 AM, Daniele Nicolodi dani...@grinta.netwrote:

 Hello Joe,

 I accidentally had a look at the source file you linked to and I found
 the copyright and license terms quite strange.  Are you really
 transferring copyright for your work to the Free Software Foundation
 which is releasing it under the MIT license?  I find very strange that
 the FSF is using a license other than the (L)GPL.  Does it also mean
 that who wants to contribute to your project must sign a copyright
 assignment to the FSF?

 I'm puzzled.

 Best,
 Daniele


 On 30/10/2013 17:58, Joe Kington wrote:
 
  On Oct 30, 2013 9:43 AM, Nils Wagner nils...@googlemail.com
  mailto:nils...@googlemail.com wrote:
 
  Hi all,
 
  How can I retrieve the corresponding color value in percent, when I
  click on the image ?
 
 
  You have to jump through a couple of hoops.
 
  Have a look at the _coords2index function in pick_info.py for
  mpldatacursor.
 
 https://github.com/joferkington/mpldatacursor/blob/master/mpldatacursor/pick_info.py
 
  Also, mpldatacursor might be useful for what you're doing. Not to plug
  my own project too much, but it does exactly this, among other things.
 
  Hope that helps!
  -Joe
 
  fig = plt.figure()
  ax  = fig.add_subplot(111)
  cax = ax.imshow(col[:,::2],
 interpolation='nearest',extent=[0.5,20.5,0.5,2
  0.5],alpha=1,picker=5)
 
  fig.canvas.mpl_connect('pick_event', onpick)
 
  def onpick(event):
   ...
 
 
  Nils
 
 
 
 
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  white
  paper to learn more about secure code signing practices that can help
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  Android apps secure.
 
 
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Re: [Matplotlib-users] pick event for images

[Joe Kington]

On Oct 30, 2013 9:43 AM, Nils Wagner nils...@googlemail.com wrote:

 Hi all,

 How can I retrieve the corresponding color value in percent, when I
click on the image ?


You have to jump through a couple of hoops.

Have a look at the _coords2index function in pick_info.py for
mpldatacursor.
https://github.com/joferkington/mpldatacursor/blob/master/mpldatacursor/pick_info.py

Also, mpldatacursor might be useful for what you're doing. Not to plug my
own project too much, but it does exactly this, among other things.

Hope that helps!
-Joe

 fig = plt.figure()
 ax  = fig.add_subplot(111)
 cax = ax.imshow(col[:,::2], interpolation='nearest',extent=[0.5,20.5,0.5,2
 0.5],alpha=1,picker=5)

 fig.canvas.mpl_connect('pick_event', onpick)

 def onpick(event):
  ...


 Nils



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Re: [Matplotlib-users] Pick event after using Zoom to rectangle

[Joe Kington]

If you're asking how to do it interactively, just click on the zoom button
again, and you should be able to fire pick events by clicking again.

Hope that helps!
-Joe
On Oct 29, 2013 4:58 AM, Nils Wagner nils...@googlemail.com wrote:

 Hi all,

 How can I use a pick event when I have used Zoom to rectangle before ?

 Nils



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Re: [Matplotlib-users] Choosing optimal figure width/height automatically

[Joe Kington]

I just realized that I replied to this off-list. Sending back out to the
entire list. (Sorry for the duplicate e-mail Christoph!)

On Oct 18, 2013 6:11 AM, Christoph Groth christ...@grothesque.org wrote:

 Joe, thank you very much for your reply.  So the figsize of a
 matplotlib plot is the physical size of the region between the axes
 where the data is shown?


No, your first assumption was correct. figsize refers to the size of the
whole figure.

What I meant to do in that example was abuse the fact that matplotlib will
happily add things beyond the figure boundaries. You can then abuse the
bbox_inches kwarg to savefig to show everything, while keeping the size
of the data area between the axes boundaries the same as the figsize.

My example there is actually completely wrong.  I meant to do this:

import numpy as np
import matplotlib.pyplot as plt

dpi = 80
data = np.random.random((100, 100))

height, width = np.array(data.shape, dtype=float) / dpi

fig = plt.figure(figsize=(width, height), dpi=dpi)
ax = fig.add_axes([0, 0, 1, 1])

ax.imshow(data, interpolation='none')
fig.savefig('test.png', bbox_inches='tight')

At any rate, I'm not quite sure if that's actually what you wanted, but
it's a useful trick in cases like this.
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Re: [Matplotlib-users] Choosing optimal figure width/height automatically

[Joe Kington]

snip


 Unfortunately, figaspect is only an approximate solution, as it simply
 uses the aspect ration of the image for the whole figure (with axes and
 labels).

 I wonder how difficult it would be to teach matplotlib to tightly fit
 the axes around an image, and, ideally, output the figure cropped.


So, you're wanting the image to be displayed pixel-to-pixel, but still have
(tight) room for the axes, etc?

If so, you can use the bbox_inches kwarg to crop out and capture the
extent of the labels, etc, and just set the figure size to exactly the size
of the image.

For example:

import numpy as np
import matplotlib.pyplot as plt

dpi = 80
data = np.random.random((100, 100))

height, width = np.array(data.shape, dtype=float) / dpi

fig, ax = plt.subplots(figsize=(width, height), dpi=dpi)
ax.imshow(data, interpolation='none')
fig.savefig('test.png', bbox_inches='tight')

If show the figure (i.e. plt.show()), the ticklabels, etc will be outside
the figure and not shown, but they will be properly saved, regardless.

Hope that helps,
-Joe





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[Matplotlib-users] Fwd: Python Session at AGU 2013

[Joe Kington]

For anyone attending the AGU (American Geophysical Union) fall meeting this
year, there will be a session on python and big data in the earth
sciences. Abstract submission is still open until Aug. 6th. See below for
more info.

Cheers,
-Joe

-- Forwarded message --
From: IRIS Webmaster webmas...@iris.washington.edu
Date: Thu, Aug 1, 2013 at 11:18 AM
Subject: [iris-bulk] Python Session at AGU 2013
To: bulkm...@iris.washington.edu


Forwarded on behalf of:
Lion Krischer
LMU Munich
krisc...@geophysik.uni-muenchen.de


Dear members of the IRIS community,

with the deadline for abstract submission to the AGU Fall Meeting 2013
approaching fast, I wanted to point out a session revolving around the
Python programming language.

If you will be attending the meeting and are using Python for your research
or workflows, please consider submitting an abstract to the IN-034 session
until *next week Tuesday, August 6th*.

https://fallmeeting.agu.org/2013/scientific-program/session-search/sessions/in034-ultra-scale-earth-systems-analyses-using-python/

It aims to promote the use of Python in the earth-science community.

All the best,

Lion Krischer and Thomas Lecocq
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Re: [Matplotlib-users] Linewidths in data space?

[Joe Kington]

 Apart from implementing this natively in matplotlib, I was thinking if
 I could work around this limitation for our purposes in the following
 way: My objects to draw will always sit in a Collection. I could derive
 my own Collection class with a draw() method that changes the
 linewidth of the collections, taking into account the current
 axes.transData and figure.dpi. Using that I should be able to
 achieve my goal, right? (given that I restrict myself to plots with
 the same scaling in all directions)


Not to plug one of my own answers, but there's an example of using a
callback to keep linewidths in data coordinates here:
http://stackoverflow.com/a/15673254/325565

Have a look at the second example in the answer for the callback approach.
Using something like ``shapely`` to buffer your lines as the first example
does is also an option.

Hope that helps a bit anyway,
-Joe
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Re: [Matplotlib-users] Evolution of the interface

[Joe Kington]

On Mar 15, 2013 10:01 AM, Christophe BAL projet...@gmail.com wrote:

 Hello,
 I really appreciate the work done by matplotlib but I really think that
 the interface must evolve. Here is a small example.

 object.set_something(...)
 object.get_something()

 It could be easier to use a jQuery like style as in the following lines.

 object(...)
 object()

 This will considerably simplify things.


 Here is a more realistic example.

 fig = pylab.figure()
 ax  = fig.add_subplot(1,1,1)

 ax.set_xlabel(xLabel)
 ax.set_ylabel(yLabel)

 The a jQuery like style would be as in the following lines.

 fig = pylab.figure()
 ax  = fig.add_subplot(1,1,1)

 ax(xlabel = xLabel, ylabel = yLabel)


Just fyi: you can always do

ax.set(xlabel=something, ylabel=something)


 I don't know enough matplotlib to propose other examples
 but I really think that there is a lot of things that could make
 matplotlib much more Pythonicly easy to use.

 Christophe BAL


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Re: [Matplotlib-users] Feedback on an implementation of a matlab-ish datacursor

[Joe Kington]

On Wed, Mar 13, 2013 at 3:22 PM, Jonathan Slavin jsla...@cfa.harvard.eduwrote:

 Nevermind on my earlier question on artists and using datacursor.  I
 figured that one out.  What I did was basically (after creating the
 image and contours):

 artist = gca().images
 datacursor(artist)

 and it worked!


Glad it worked! For what it's worth, you can also do:

im = imshow(yourdata)
datacursor(im)

It's a bit cleaner than getting the AxesImage through ax.images.

Cheers!
-Joe


 Jon

 On Wed, 2013-03-13 at 15:50 -0400, Jonathan Slavin wrote:
  Joe,
 
  Thank you!  I will especially use it to get the z value in images.  I
  started to try to do something like this once but never finished.
 
  One thing I'm having a bit of trouble with is providing an artist as an
  argument.  The reason I wanted to do that is to look only at the values
  for the image and not those for the contours drawn on the image.  How
  does that work exactly?
 
  As a side note, I thought that I had found a bug because an I was
  looking at image would, in some places, print only the x and y values
  but not the z value.  Then I realized it was printing the values for a
  contour that I had made invisible by setting its edgecolor to 'None'.
  This was because the contour created had two parts and I only wanted to
  show one of them.  Anyone know a different way to do that?
 
  Regards,
  Jon
 
  On Tue, 2013-03-12 at 22:58 -0500, Joe Kington wrote:
   I recently got around to polishing up a snippet I've been using for
   quite awhile.  https://github.com/joferkington/mpldatacursor/  and I
   was hoping to get some feeding on the current implementation.
  
  
   mpldatacursor allows a user to easily click on an artist and display
   a customizable, interactive pop-up box displaying information about
   the selected artist (e.g. x  y, label, z for images and collections,
   etc).  It's a stand-alone module (and in pypi), but you could also
   just download the examples directory from github and copy the
   mpldatacursor.py file into it to try things out.
  
  
   A few key questions:
  
1. Is this something that anyone else finds useful?
  
2. Does it seem intuitive?
  
3. Does the implementation seem flexible enough for most needs?
   (Note that any additional kwargs are passed on to annotate to
   create the data cursor, so the appearance of the box is
   customizable through annotation kwargs.)
  
4. Are there any obvious features missing?
  
5. Any suggestions? (especially better name suggestions...)
  
   If it is something that other people find useful, I'd be happy to
   submit a pull request to incorporate it into matplotlib.  (If I did,
   it would probably be best to drop the HighlightDataCursor class, as
   its limited in what it can do.)
  
   Thanks a bunch!
  
   -Joe
  
  
  
 

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[Matplotlib-users] Feedback on an implementation of a matlab-ish datacursor

[Joe Kington]

I recently got around to polishing up a snippet I've been using for quite
awhile.  https://github.com/joferkington/mpldatacursor/  and I was hoping
to get some feeding on the current implementation.

mpldatacursor allows a user to easily click on an artist and display a
customizable, interactive pop-up box displaying information about the
selected artist (e.g. x  y, label, z for images and collections, etc).
It's a stand-alone module (and in pypi), but you could also just download
the examples directory from github and copy the mpldatacursor.py file into
it to try things out.

A few key questions:

   1. Is this something that anyone else finds useful?

   2. Does it seem intuitive?

   3. Does the implementation seem flexible enough for most needs? (Note
   that any additional kwargs are passed on to annotate to create the data
   cursor, so the appearance of the box is customizable through annotation
   kwargs.)

   4. Are there any obvious features missing?

   5. Any suggestions? (especially better name suggestions...)

If it is something that other people find useful, I'd be happy to submit a
pull request to incorporate it into matplotlib.  (If I did, it would
probably be best to drop the HighlightDataCursor class, as its limited in
what it can do.)

Thanks a bunch!

-Joe
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Re: [Matplotlib-users] Undocumented transform API change between 1.1 and 1.2?

[Joe Kington]

On Mon, Dec 10, 2012 at 2:45 AM, Phil Elson pelson@gmail.com wrote:

 Hi Joe,

 Thanks for bringing this up, it is certainly valuable to highlight this on
 the mailinglist. As you say, the change is hard to spot and, I agree, makes
 library code supporting v1.1.1 and v1.2 harder than one would like.
 Typically, anything which is going to break core APIs (even slightly)
 should be documented under the API Changes page here
 http://matplotlib.org/api/api_changes.html#changes-in-1-2-x .


Thanks! I wasn't aware of that page! (and it does a very nice job of
documenting the changes!)


 You will find there were quite a few changes made relating to transforms
 which I think is entirely my doing, so at least we know who the guilty
 party is :-)

 Thanks for spotting the example failure - I split these changes over many
 separate pull requests and did scan the gallery for any noticeable changes,
 but this one must have slipped the net.

 If you're still having problems with using the newer transform API, please
 shout and I'd be happy to have a look for you.


Will do, thanks for the offer!



 All the best,

 Phil


 On 9 December 2012 22:10, Joe Kington joferking...@gmail.com wrote:

 Hi folks,

 At some point transforms.Transform was slightly refactored.
 (Particularly, this commit:
 https://github.com/matplotlib/matplotlib/commit/8bbe2e55f29b28ba558504b27596b8e36a087c1c)
   This changed what methods need to be overridden when subclassing
 Transform.

 All in all, it seems like a very sensible change, but it led to some very
 hard-to-find bugs in some of my code that subclasses transforms.Transform.
 I thought I would mention it on the mailing list for anyone else that uses
 custom projections. Forgive me if it was mentioned earlier and I just
 didn't notice.

 With versions 1.1.x and older, one had to directly implement a 
 transformmethod when subclassingtransforms.Transform,
 otherwise a NotImplemented error would be raised.

 With versions 1.2.x and newer, the preferred way appears to be to
 implement things in a separate transform_affine or 
 transform_non_affinemethod and not explicitly implement a
 transform method.

 If you implement the non-affine portion directly in the transform method
 without overriding transform_non_affine, it leads to strange drawing
 bugs with v1.2 that did not occur with older versions.  (For example, this
 broke one of the examples in the gallery between 1.1. and 1.2:
 http://matplotlib.org/1.1.1/examples/api/custom_projection_example.html
 http://matplotlib.org/1.2.0/examples/api/custom_projection_example.html. I 
 just submitted a pull request to update the example, by the way.)

 On the other hand, for compatibility with versions 1.1 and older, you
 have to explicitly implement the transform method as well, otherwise
 you'll get the NotImplemented error.

 Therefore, now one needs to explicitly implement *_both_* the
 transform_non_affine and transform methods of a custom non-affine
 transform for compatibility with 1.1 and older as well as 1.2 and newer.

 Similarly, one needs to implement* _both_ *the transform_path_non_affineand 
 the
 transform_path methods for compatibility with newer and older versions
 of matplotlib.

 Arguably, it should have always been done this way, but  based 
 onexamples/api/custom_projection_example.py,
 I (and I suspect many other people as well)  implemented the transform
 directly as the transform method when subclassing Transform, instead of
 separately in a transform_affine or transform_non_affine method.

 Is this a large enough change to warrant a mention in the changelog? (On
 the other hand, the mailing list probably gets a lot more eyes on it than
 the changelog...)

 Thanks!
 -Joe



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Re: [Matplotlib-users] plot with marker color coded according to z-value

[Joe Kington]

That's what ``scatter`` is intended for.

Basically, you want something like:

plt.scatter(x, y, c=z, marker='s')
plt.colorbar()

Note that you can also vary the markers by size based on an additional
parameter, as well.

Have a look at this example:
http://matplotlib.org/examples/pylab_examples/scatter_demo.html

Hope that helps,
-Joe


On Fri, Oct 19, 2012 at 2:19 PM, elmar werling el...@net4werling.de wrote:

 Hi,

 is there a way to adjust the marker color in a xy-plot in relation to
 the value of a third parameter. Something as the following - not working
 - example 1.

 Example 2 is working but rather slow for large arrays.

 cheers
 Elmar




 # example 1

 import matplotlib.pyplot as plt

 x = [1,2,3,4]
 y = x
 c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3))

 plt.plot(x,y, color=c, marker='s')
 plt.show()


 example 2:

 import matplotlib.pyplot as plt

 x = [1,2,3,4]
 y = x
 c = ((1.0, 0.0, 0.0), (0.8, 0.1, 0.1), (0.6, 0.2, 0.6), (0.4, 0.3, 0.3))

 for i in range(len(x)):
  plt.plot(x[i], y[i], color=c[i], marker='s')

 plt.show()



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[Matplotlib-users] ANN: mplstereonet v0.2 - Stereonets for matplotlib

[Joe Kington]

Announcement - mplstereonet v0.2
=

mplstereonet provides lower-hemisphere equal-area and equal-angle
stereonets for matplotlib.

Download
--

Available for download at: http://pypi.python.org/pypi/mplstereonet/0.2

See the github page for more information and development versions:
https://github.com/joferkington/mplstereonet

About
-

Stereonets are a type of plot commonly used in the geosciences for
visualizing and analyzing orientation data.

Orientations of linear and planar features are plotted as the intersection
of the feature with a hemisphere.  (Therefore, linear features plot as
points and planar features plot as lines.)

mplstereonet provides plotting of planes (measured as strikes and dips),
poles to planes, and lineations (measured as rakes along a plane or as
plunges and bearings).

Additionally, mplstereonet provides orientation density contouring using a
variety of standard methods (e.g. Kamb contouring, etc).

Getting Started
--

The easiest way to get started is to have a look at the examples:
https://github.com/joferkington/mplstereonet/tree/master/examples

Additionally, the readme has an overview and various examples:
https://github.com/joferkington/mplstereonet/blob/master/README.rst

Changes
-

New features in version 0.2 include:

- Density contouring of orientation data using a variety of methods
- Equal-angle as well as equal-area stereonets
- A variety of parsing utilities
- Geometric analysis utilities (e.g. plane intersections)

Thank you all very much!
-Joe Kington
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Re: [Matplotlib-users] Newbie-question: spines with arrows

[Joe Kington]

It sounds like you were using the right approach, you just got a bit lost
on what some of the keyword parameters to annotate, etc do.

Here's an example that should do what you want:

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator

fig, ax = plt.subplots()

# Set the axis limits as you wanted...
ax.set_ylim([-0.6, 1.1])

# Set major and minor tick intervals
# We could just set the tick locations manually, but this way you'll keep
the
# same intervals when you zoom/pan/etc.
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))

# Make the bottom spine float instead of being at the bottom of the plot
ax.spines['bottom'].set_position(('data', 0))

# Hide the top and right spines (Similar to your sketch)
for position in ['right', 'top']:
ax.spines[position].set_visible(False)

# Turn off the top, right, and bottom major and minor ticks (as in your
sketch)
ax.tick_params(which='both', top=False, right=False, bottom=False,
   labelbottom=False)

# Draw a 15 point long arrow that will always be at the top-left corner of
# the axes.  The key is that we're specifying a location of (0,1) in
# axes fraction coordinates.  We then place an empty text string 15
_points_
# above this (the `xytext` parameter controls the amount, `textcoords`
controls
# how it's interpreted).  Then, we draw an arrow connecting the top left
corner
# of the axes to the (empty and not drawn) text string.
ax.annotate('', xy=(0,1), xycoords='axes fraction', xytext=(0,15),
textcoords='offset points', ha='center',
arrowprops=dict(arrowstyle='|-', shrinkA=0, shrinkB=0,
   facecolor='black'))
plt.show()


Hope that helps!
-Joe

On Fri, Jun 15, 2012 at 7:34 AM, Asbach, Mark 
mark.asb...@iais.fraunhofer.de wrote:

  Hi there,

 I'm sorry to ask such a newbie question, but I'd like to format a custom
 box plot and although there are numerous examples on the web and tons of
 docstrings in matplotlib, I'm stuck somehow. My problems center around axes
 / spines. In detail, my problems are:

 1) I want an y-axis on the left that spans from -0.6 to 1.1, ends in an
 arrow, has major ticks at 0 and 1 and minor ticks at [0.1...0.9]
 As far as I understand, there is no option to let spines end in an arrow
 head, so I have to draw the myself. I get the ticks to appear at the right
 positions and the y-range to be as desired - however, the spine line is not
 drawn over the full y-range, but only where there is data in the diagram.
 Also, I copied the arrow annotation code blindly from an older post on this
 list, but do not understand how I can adapt the arrow head to appear at a
 data position (instead of at the corner of the Axes area). One problem is,
 that I get ticks on the right although that spine was disabled.

 2) I want some kind of x-axis at y==0, without ticks and without arrow
 Using some methods on the spines, I can disable the top spine and move the
 bottom spine to zero. However, as with the y-axis, I cannot control from
 where to where the line itself is drawn.

 As attachments, you'll find a hand sketch of what my graph should look
 like and matplotlib code that goes nearly all the way.

 I would be very happy about a hint on how to fix the problems left.

 Thanks an advance,
 Mark



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Re: [Matplotlib-users] Time axis for imshow

[Joe Kington]

On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever gokhanse...@gmail.com wrote:

 Hello,

 Is there any easy way to specify a time-axis using imshow to plot 2D data?


Sure, just call ax.xaxis_date() (or yaxis_date, depending on which axis
you want to represent a date).

As a quick example:

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

# Generate data...
ny = 100
xmin, xmax = mdates.datestr2num(['01/01/2011', '11/10/2011'])
data = np.random.random((ny, int(xmax-xmin)+1)) - 0.5
data = data.cumsum(axis=1)

# Plot...
fig, ax = plt.subplots()
ax.imshow(data, extent=[xmin, xmax, 0, ny])
ax.xaxis_date()
fig.autofmt_xdate()

plt.show()

Cheers,
-Joe
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Re: [Matplotlib-users] Time axis for imshow

[Joe Kington]

snip

 Although, this doesn't give me millisecond precision. Is there any way to
 get ms precision via datetime module?

 snip

Well, datetime objects, matplotlib's internal float dates, and numpy
datetime64 objects all support microsecond resolution.

However matplotlib's locator rules can't handle microsecond or millisecond
resolution. There aren't any locators for less than second resolution.

Also, imshow sets the aspect of the plot to 1 by default, which is probably
why you're having to set the extents manually. If you specify
aspect='auto' in the imshow call you can avoid that step.  (However,
strange things happen when the span of the extents drops below 100
microseconds... I'm guessing something is being cast to float32's
somewhere?)


As a quick example to demonstrate using sub-second resolution (without a
proper tick locator):

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from datetime import datetime

# Generate data...
ny = 100
nx = 100
xmin, xmax = mdates.date2num([datetime(2011, 01, 01, microsecond=1),
  datetime(2011, 01, 01, microsecond=nx)])
data = np.random.random((ny, nx)) - 0.5
data = data.cumsum(axis=1)

# Plot...
fig, ax = plt.subplots()
ax.imshow(data, aspect='auto', extent=[xmin, xmax, 0, ny])
ax.xaxis_date()

plt.show()

At any rate, you can write a quick-and-dirty millisecond locator...  Give
me a bit and I'll cobble one together. (It's turning out to be slightly
more complex than I thought...)


On Thu, Nov 10, 2011 at 10:06 AM, Gökhan Sever gokhanse...@gmail.comwrote:

 Thanks Joe,

 I forgot to convert my numeric time array into a form that mpl can
 understand.

 I198 time
 O198
 array([ 32643.78595805,  32643.82032609,  32643.85445309, ...,
 32871.46535802,  32871.49946594,  32871.53384495])

 I199 ncnt
 O199
 array([0001-01-01 09:04:03+00:00, 0001-01-01 09:04:03+00:00,
0001-01-01 09:04:03+00:00, ..., 0001-01-01 09:07:51+00:00,
0001-01-01 09:07:51+00:00, 0001-01-01 09:07:51+00:00], dtype=object)

 Although, this doesn't give me millisecond precision. Is there any way to
 get ms precision via datetime module?
 This is not a matter for plotting, since second precision is good enough
 for eyes.

 Then setting extent properly and either calling ax.xaxis_date or calling
 setters manually

 I196 xmin = mdates.date2num(ncnt[0])

 I197 xmax = mdates.date2num(ncnt[-1])

 plt.imshow(z.T, interpolation='nearest', aspect='auto', origin='lower',
 extent=[xmin, xmax, 0, z.shape[1]])

 ax = plt.gca()
 ax.xaxis.set_major_formatter(DateFormatter('%H:%M:%S'))
 ax.xaxis.set_major_locator(SecondLocator(interval=30))
 ax.xaxis.set_minor_locator(SecondLocator(interval=5))

 gives me better control over the major/minor ticks.


 On Thu, Nov 10, 2011 at 8:15 AM, Joe Kington jking...@wisc.edu wrote:

 On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever gokhanse...@gmail.comwrote:

 Hello,

 Is there any easy way to specify a time-axis using imshow to plot 2D
 data?


 Sure, just call ax.xaxis_date() (or yaxis_date, depending on which
 axis you want to represent a date).

 As a quick example:

 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 import numpy as np

 # Generate data...
 ny = 100
 xmin, xmax = mdates.datestr2num(['01/01/2011', '11/10/2011'])
 data = np.random.random((ny, int(xmax-xmin)+1)) - 0.5
 data = data.cumsum(axis=1)

 # Plot...
 fig, ax = plt.subplots()
 ax.imshow(data, extent=[xmin, xmax, 0, ny])
 ax.xaxis_date()
 fig.autofmt_xdate()

 plt.show()

 Cheers,
 -Joe




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Re: [Matplotlib-users] Newbie question

[Joe Kington]

On Wed, Nov 9, 2011 at 10:07 AM, Howard how...@renci.org wrote:

  Hi all

 I'm a new user to matplotlib, and I'm having a little difficulty with
 something I feel must be basic. When I plot our data, I'm using a canvas
 that is 4x4 at 128 DPI and saving the canvas as a png.  Here's the basics
 of the code:

imageWidth = 4
imageHeight = 4
DPI = 128

figure1 = plt.figure(figsize=(imageWidth,imageHeight))
plt.axis(off)
plt.tricontourf(theTriangulation,
modelData,
theLookupTable.N,
cmap=theLookupTable)
canvas = FigureCanvasAgg(figure1)
canvas.print_figure(prefix + .png, dpi=DPI)

 The png is 512x512 as I would expect, but the contoured image doesn't fill
 the whole image.  How do I tell the library to map the plotted are to the
 entire canvas and not leave a border around the rendered image?


You need to make an axis that fills up the entire figure.

By default, axes don't fill up the entire figure to leave room for tick
labels, axis lables, titles, etc.

Try something like:

import matplotlib as plt

dpi = 128
fig = plt.figure(figsize=(4,4))

# Specifies an axis at 0, 0 with a width and height of 1 (the full width of
the figure)
ax = fig.add_axes([0,0,1,1])

ax.tricontourf(...)

fig.savefig('output.png', dpi=dpi)

 Hope that helps,
-Joe
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Re: [Matplotlib-users] Newbie question

[Joe Kington]

On Wed, Nov 9, 2011 at 10:20 AM, Howard how...@renci.org wrote:

  On 11/9/11 11:13 AM, Joe Kington wrote:

 On Wed, Nov 9, 2011 at 10:07 AM, Howard how...@renci.org wrote:

  Hi all

 I'm a new user to matplotlib, and I'm having a little difficulty with
 something I feel must be basic. When I plot our data, I'm using a canvas
 that is 4x4 at 128 DPI and saving the canvas as a png.  Here's the basics
 of the code:

imageWidth = 4
imageHeight = 4
DPI = 128

figure1 = plt.figure(figsize=(imageWidth,imageHeight))
plt.axis(off)
plt.tricontourf(theTriangulation,
modelData,
theLookupTable.N,
cmap=theLookupTable)
canvas = FigureCanvasAgg(figure1)
canvas.print_figure(prefix + .png, dpi=DPI)

 The png is 512x512 as I would expect, but the contoured image doesn't
 fill the whole image.  How do I tell the library to map the plotted are to
 the entire canvas and not leave a border around the rendered image?


 You need to make an axis that fills up the entire figure.

 By default, axes don't fill up the entire figure to leave room for tick
 labels, axis lables, titles, etc.

 Try something like:

 import matplotlib as plt

 dpi = 128
 fig = plt.figure(figsize=(4,4))

 # Specifies an axis at 0, 0 with a width and height of 1 (the full width
 of the figure)
 ax = fig.add_axes([0,0,1,1])

 ax.tricontourf(...)

 fig.savefig('output.png', dpi=dpi)

  Hope that helps,
 -Joe

 Hi Joe

 That did it!  Thanks much. Can I also turn off the tick marks on the new
 axis?


Sure! If you want an exact duplicate of your original code snippet, just
add an:

ax.axis('off')

or one of the many equivalent ways of hiding the entire axis.

If you'd rather keep the outline and white background patch, but just turn
the ticks off, then you can do something like:

for axis in [ax.xaxis, ax.yaxis]:
ax.set_ticks([])

or similarly:

ax.tick_params(color='none')

Also, as you've probably already noticed, I meant to have import
matplotlib.pyplot as plt in my first reply, rather than import matplotlib
as plt.

Cheers!
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Re: [Matplotlib-users] Ternary Plot

[Joe Kington]

The link on Nabble is broken, so here's (I think) a fixed version. It looks
like the name of the branch was changed slightly at some point.

https://github.com/kdavies4/matplotlib/compare/master...ternary2

Cheers,
-Joe



On Thu, Nov 3, 2011 at 3:14 PM, Benjamin Root ben.r...@ou.edu wrote:



 On Thu, Nov 3, 2011 at 2:58 PM, elmar werling el...@net4werling.dewrote:

 Hi,

 could not found anything like plot_ternary() in the matplotlib
 documentation. Is there an easy to use method to plot data points and
 lines in a ternary plot using matplotlib?

 Any hint is wellcome

 Elmar


 This is a requested feature, but has not been implemented yet within
 matplotlib.  However, some other users have created a hack to do this:

 http://old.nabble.com/Ternary-or-triangle-plots-td32506324.html

 Ben Root



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Re: [Matplotlib-users] how to hide axes in a 3D plot

[Joe Kington]

Interestingly, things work perfectly with the latest build from guithub.
Presumably the bug was fixed already?

On Wed, Sep 7, 2011 at 2:40 PM, Matthew Koichi Grimes m...@cs.nyu.eduwrote:

 I tried Joe's code, with the call to ax.set_axis_off() moved to right after
 add_subplot(), as Ben suggested. The axes are still not disappearing, nor do
 they disappear when I interact with it (by rotating the plot).

 -- Matt


 On Wed, Sep 7, 2011 at 3:21 PM, Benjamin Root ben.r...@ou.edu wrote:

 On Wed, Sep 7, 2011 at 2:14 PM, Joe Kington jking...@wisc.edu wrote:

 This no longer seems to work with matplotlib 1.0.1.

 As a quick example:

 import numpy as np
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D

 fig = plt.figure()
 ax = fig.add_subplot(111, projection='3d')

 x,y,z,c = np.random.random((4,10))
 ax.scatter(x, y, z, c=c)
 ax.set_axis_off()

 plt.show()

 The attached .png shows the result on my system... Is this a bug, or am I
 doing something strange?

 Thanks!
  -Joe


 Hmmm, try putting that call right after the add_subplot() call.  I don't
 have time to test it out right now, but I wonder if the axes are being drawn
 once prior to the call to set_axis_off().  I would also be interested to
 know if the axes disappear when you interact with it.

 Ben Root



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Re: [Matplotlib-users] Plotting in loop problem, not refreshing

[Joe Kington]

Your code should work (and does on my system)...

What backend, version of matplotlib, OS, etc are you running?

On Sat, Jun 4, 2011 at 4:54 PM, Armin G armi...@gmail.com wrote:


 Hi everyone ,

 I know this has been posted several times now, But I could not understand
 qietly why my simple code does not work.
 here is the code:
 http://old.nabble.com/file/p31775254/LinearConvection.pyLinearConvection.py

 The problem in detail: In the solver loop ( the outer loop) , The plot
 should be updated after every time step, but it stays the same.

 I'm a complete noob in python, so excuse my simple communicating language.

 thanks alot,
 Armin
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 Sent from the matplotlib - users mailing list archive at Nabble.com.



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Re: [Matplotlib-users] Getting axis limits through ax.axis() turns off autoscaling. Confusing behavior?

[Joe Kington]

Thanks!
-Joe

On Sun, May 15, 2011 at 12:50 AM, Eric Firing efir...@hawaii.edu wrote:

 On 05/14/2011 12:22 PM, Joe Kington wrote:
  Hi,
 
  When getting an axis's extents through axis, the autoscaling state of
  the axis is turned off, regardless of the state it was in before calling
  ax.axis()
 
  E.g.
  import matplotlib.pyplot as plt
  fig = plt.figure()
  ax = fig.add_subplot(111)
  print ax.get_autoscale_on()
  limits = ax.axis()
  print ax.get_autoscale_on()
 
  It makes sense that it would be turned off if the axis's limits are
  manually set, but calling ax.get_xlim() or ax.get_ylim() doesn't change
  the autoscaling state, so why should getting the extents by calling
  ax.axis()?

 It is now fixed in v1.0.x and master on github.

 Eric
 
  This seems like confusing and/or inconsistent behavior to me. Would this
  be considered a bug?  If not, is it worth clarifying in the docstring to
  axis?
 
  Thanks,
  -Joe
 
 
 
 
 
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Re: [Matplotlib-users] Pixel shape

[Joe Kington]

Actually, I think he's wanting a set aspect, right?  Either way, it's just
aspect=1.5 or aspect=0.6667 depending on the orientation he wants.

On Mon, Apr 18, 2011 at 6:37 AM, Sebastian Berg
sebast...@sipsolutions.netwrote:

 The solution is already the aspect='auto', ie:

 import numpy as np
 from matplotlib import pyplot as plt
 a = np.arange(100).reshape(10,10)
 plt.imshow(a, aspect='auto')

 aspect='auto' is what you were looking for, the documentation (as you
 probably already found is for example at:

 http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.imshow
 or in interactive help.


 On Sun, 2011-04-17 at 23:16 +0200, Paolo Zaffino wrote:
  Thanks for the reply.
  I checked in the help...I didn't understand what I must to use.
  Should you post me the link of the guide of this setting?
  Thanks!
 
 
  Il 16/04/2011 10:47, Sebastian Berg ha scritto:
   Hello,
  
   check the help ;). you can set aspect='auto' or something fixed.
  
   Regards,
  
   Sebastian
  
   On Sat, 2011-04-16 at 10:43 +0200, Paolo Zaffino wrote:
   Hi at all,
   I have a numpy matrix (an image) and I'd like to show it.
   I thought to use show function, but I have a question.
   I don't want that the pixel have dimension 1x1 unit but I want for
   example 1X1.5 unit (I don't want a square but a rectangle).
   How can I do this?
   Thanks in advance.
   Paolo
  
  
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Re: [Matplotlib-users] Auto-wrapping text within a plot... Is there a simpler solution?

[Joe Kington]

For whatever it's worth, after a lot of wrangling, I think I solved most of
my problems (though perhaps not in the most efficient way).

In case anyone else is looking for similar functionality, here's a callback
function that will autowrap text objects to the inside of the axis they're
plotted in, and should handle any font, rotation, etc that you throw at it.
(The previous version had a lot of bugs).

Hope someone finds it useful, at any rate...
-Joe
[image: E2G1j.png]

import matplotlib.pyplot as plt

def main():
Draw some very long strings on a figure and have the auto-wrapped
to the axis boundaries. Try resizing the figure!!

fig = plt.figure()
plt.axis([0, 10, 0, 10])

t = This is a really long string that I'd rather have wrapped so\
 that it doesn't go outside of the figure, but if it's long\
 enough it will go off the top or bottom!

t2 = rFurthermore, if I put mathtext in here, it won't mutilate it,\
 but will treat it like a really long word. For example: \
r$\frac{\sigma}{\gamma} - e^{\theta \pm 5}$ won't be mangled!

plt.text(5, 10, t2, size=14, ha='center', va='top', family='monospace')
plt.text(3, 0, t, family='serif', style='italic', ha='right')

plt.text(4, 1, t, ha='left', family='Times New Roman', rotation=15)
plt.text(5, 3.5, t, ha='right', rotation=-15)

plt.title(This is a really long title that I want to have wrapped so\
 r it does not go outside the axis boundaries, ha='center')

# All we do to autowrap everything  is connect a callback function...
fig.canvas.mpl_connect('draw_event', on_draw)

plt.show()

def on_draw(event):
Auto-wraps all text objects in a figure at draw-time
import matplotlib as mpl
fig = event.canvas.figure

# Cycle through all artists in all the axes in the figure
for ax in fig.axes:
for artist in ax.get_children():
# If it's a text artist, wrap it...
if isinstance(artist, mpl.text.Text):
autowrap_text(artist, event.renderer)

# Temporarily disconnect any callbacks to the draw event...
# (To avoid recursion)
func_handles = fig.canvas.callbacks.callbacks[event.name]
fig.canvas.callbacks.callbacks[event.name] = {}
# Re-draw the figure..
fig.canvas.draw()
# Reset the draw event callbacks
fig.canvas.callbacks.callbacks[event.name] = func_handles

def autowrap_text(textobj, renderer):
Wraps the given matplotlib text object so that it doesn't exceed the
boundaries of the axis it is plotted in.
# Get the starting position of the text in pixels...
x0, y0 = textobj.get_transform().transform(textobj.get_position())
# Get the extents of the current axis in pixels...
clip = textobj.get_axes().get_window_extent()
# Set the text to rotate about the left edge (nonsensical otherwise)
textobj.set_rotation_mode('anchor')

# Get the amount of space in the direction of rotation to the left and
# right of x0, y0 (left and right are relative to the rotation)
rotation = textobj.get_rotation()
right_space = min_dist_inside((x0, y0), rotation, clip)
left_space = min_dist_inside((x0, y0), rotation - 180, clip)

# Use either the left or right distance depending on the h-alignment.
alignment = textobj.get_horizontalalignment()
if alignment is 'left':
new_width = right_space
elif alignment is 'right':
new_width = left_space
else:
new_width = 2 * min(left_space, right_space)

# Convert to characters with a minimum width of 1 character
wrap_width = max(1, new_width // pixels_per_char(textobj))
try:
wrapped_text = safewrap(textobj.get_text(), wrap_width)
except TypeError:
# This appears to be a single word
wrapped_text = textobj.get_text()
textobj.set_text(wrapped_text)

def min_dist_inside(point, rotation, box):
Gets the space in a given direction from point to the boundaries
of box (where box is an object with x0, y0, x1,  y1 attributes,
point is a tuple of x,y, and rotation is the angle in degrees)
from math import sin, cos, radians
x0, y0 = point
rotation = radians(rotation)
distances = []
threshold = 0.0001
if cos(rotation)  threshold:
# Intersects the right axis
distances.append((box.x1 - x0) / cos(rotation))
if cos(rotation)  -threshold:
# Intersects the left axis
distances.append((box.x0 - x0) / cos(rotation))
if sin(rotation)  threshold:
# Intersects the top axis
distances.append((box.y1 - y0) / sin(rotation))
if sin(rotation)  -threshold:
# Intersects the bottom axis
distances.append((box.y0 - y0) / sin(rotation))
return min(distances)

def pixels_per_char(textobj):
Determines the average width of a character of the given textobj
by drawing a test string and calculating it's length
test_text = 'Try something like a test'
   

[Matplotlib-users] Auto-wrapping text within a plot... Is there a simpler solution?

[Joe Kington]

Hi folks,

First off, I apologize for the wall of text...

Spurred on by this Stack Overflow
questionhttp://stackoverflow.com/questions/4018860/text-box-in-matplotlib/4056853#4056853,
and by an itch I've been wanting to scratch lately, I put together a a
callback function that (attempts, anyway) to auto-wrap text artists to the
boundaries of the axis they're in.

It is often useful to have text reflow/auto-wrap within the axis boundaries
during interactive use (resizing a plot with lots of labeled points, for
example...).  It doesn't really need to be precise, as long as it keeps
words from being fully off the figure.

A full gui toolkit would be a better way of handling this, but I've gotten
in the habit of slapping a few callback functions onto matplotlib figures to
make (simple) interactive scripts that I can share across platforms. (Lab
exercises, in my case.) Having a way to make text reflow within the axis
boundaries during resizing of plots makes things a bit easier.

I'm aware that this isn't really possible in a general, backend-independent
fashion due to the subtleties of text rendering in matplotlib (e.g. things
like latex, where the length of the raw string has nothing to do with it's
rendered size, and the general fact that the size of the text isn't known
until after it's drawn).Even getting it approximately correct is still
useful, though.

I have it working about as well as the approach I'm taking can do, I think,
but I could use some help on a couple of points.

I have two specific questions, and one more general one...

First:
Is it possible to disconnect and then reconnect a callback function from an
event within the callback function, and without disconnecting all other
callback functions from the event?

I'm redrawing the canvas within a draw_event callback function, and I'm
currently avoiding recursion by disconnecting and reconnecting all callbacks
to the draw event. It would be nice to disconnect only the function I'm
inside, but I can't find any way of getting its cid...

Alternatively, is there a way to redraw a figure's canvas without triggering
a draw event?

Second:
Is there any way to determine the average aspect ratio of a font in
matplotlib?

I'm trying to approximate the length of a rendered text string based on it's
font size and the number of characters. Currently, I'm assuming that all
fonts have an average aspect ratio of 0.5, which works decently for most
non-monospaced fonts, but fails miserably with others.

Finally:
Is there a better way to do this? My current approach has tons of
limitations but works in most situations.  My biggest problem so far is that
vertical alignment in matplotlib (quite reasonably) refers to an
axis-aligned bounding box, rather than within a text aligned bounding box.
This makes reflowing rotated text more difficult, and I'm only half-way
dealing with rotated text in the code below.  Any suggestions on any points
are welcome!

Thanks!
-Joe

import matplotlib.pyplot as plt

def main():
fig = plt.figure()
plt.plot(range(10))

t = This is a really long string that I'd rather have wrapped so that
it\
 doesn't go outside of the figure, but if it's long enough it will go\
 off the top or bottom!
plt.text(7, 3, t, ha='center', rotation=30, va='center')
plt.text(5, 7, t, fontsize=18, ha='center')
plt.text(3, 0, t, family='serif', style='italic', ha='right')
plt.title(This is a really long title that I want to have wrapped so
it\
  does not go outside the figure boundaries)
fig.canvas.mpl_connect('draw_event', on_draw)
plt.show()

def on_draw(event):
import matplotlib as mpl
fig = event.canvas.figure

# Cycle through all artists in all the axes in the figure
for ax in fig.axes:
for artist in ax.get_children():
# If it's a text artist, wrap it...
if isinstance(artist, mpl.text.Text):
autowrap_text(artist, event.renderer)

# Temporarily disconnect any callbacks to the draw event...
# (To avoid recursion)
func_handles = fig.canvas.callbacks.callbacks[event.name]
fig.canvas.callbacks.callbacks[event.name] = {}
# Re-draw the figure..
fig.canvas.draw()
# Reset the draw event callbacks
fig.canvas.callbacks.callbacks[event.name] = func_handles

def autowrap_text(textobj, renderer):
import textwrap
from math import sin, cos
# Get the starting position of the text in pixels...
x0, y0 = textobj.get_transform().transform(textobj.get_position())
# Get the extents of the current axis in pixels...
clip = textobj.get_axes().get_window_extent()

# Get the amount of space in the direction of rotation to the left and
# right of x0, y0
# (This doesn't try to correct for different vertical alignments, and
will
# have issues with rotated text when va  ha are not 'center')
dx1, dx2 = x0 - clip.x0, clip.x1 - x0
dy1, dy2 = y0 - clip.y0, clip.y1 - y0
rotation = 

Re: [Matplotlib-users] How to find out the extend of the actual image in pixels

[Joe Kington]

If you're just looking for the dimensions of the overall figure in pixels,
you can easily access them through other means. E.g:

import matplotlib.pyplot as plt
fig = plt.figure()
_, _, width, height = fig.bbox.extents # - Extent of the figure in pixels
fig.savefig('temp.png', dpi=fig.dpi) # - Be sure to specify the dpi!!

Note that you'll have to specify that you want to save the figure at the
same DPI it was when you called the extents...

The figure will be saved with whatever default DPI is in your .matplotlibrc,
which is commonly different than the default screen dpi (80).

Hope that helps,
-Joe

On Wed, Sep 29, 2010 at 9:44 AM, Alexander Dietz 
alexanderdie...@googlemail.com wrote:

 Hi,

 On Wed, Sep 29, 2010 at 16:00, John Hunter jdh2...@gmail.com wrote:

 On Wed, Sep 29, 2010 at 8:50 AM, Alexander Dietz
 alexander.diet...@googlemail.com wrote:

  print ax.transData.transform((10.0, 20.0))
  [ 576.  432.]


 Why do you say it's wrong?  Note that in mpl, (0,0) is (bottom left),
 not (upper,left).  So this is saying that the yellow dot at 10,20
 (data coords) is 576 pixels up from the bottom and 432 pixels over
 from the left.



 ok maybe it is. But then I need the size of the entire figure written to a
 file. How can I find out the pixel-size of the entire figure, inclusive
 every title, axis, labels...


 Thanks
   Alex




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Re: [Matplotlib-users] sector colouring?

[Joe Kington]

Have a look at fill_between:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.fill_between

Basically, You'd want something like this:

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, np.pi, 20)
y = np.sin(x)
plt.figure()
plt.fill_between(x, y, where=y0, facecolor='blue', interpolate=True)
plt.fill_between(x, y, where=y0, facecolor='yellow', interpolate=True)
plt.show()

Hope that helps!
-Joe

On Mon, Sep 27, 2010 at 8:10 AM, Piter_ x.pi...@gmail.com wrote:

 Hi all.
 Is it possiblle in matplotlib to draw something like this?

 http://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/Integral_example.svg/420px-Integral_example.svg.png
 Thanks.
 Petro.



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Re: [Matplotlib-users] Adding a Legend

[Joe Kington]

On Sun, Sep 19, 2010 at 10:40 PM, Kelson Zawack zawack...@gis.a-star.edu.sg
 wrote:

  Is there a way to put the legend for a graph inside the margin instead
 of on the graph, in other words to put the legend where excel would?


Look into either
figlegendhttp://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.figlegend
or the bbox_to_anchor keyword argument to legend.  You can use either of
these to place the legend outside of the axis boundaries.  However, this
won't resize the original axes.

Sometimes, it's easier to just make a new, separate axis for the legend, and
resize the original axis.  This is easily done with the axes_grid toolkit.
E.g.:

import matplotlib.pyplot as plt

from matplotlib import cm
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable

# Make some data
data = np.random.random(10)
x = np.arange(data.size)
colors = cm.jet(x.astype(np.float) / x.size)
names = [repr(i) for i in range(data.size)]

# Basic bar plot
fig = plt.figure()
ax = fig.add_subplot(111)

bars = ax.bar(x, data, color=colors)

# Now make a new axis for the legend that takes up 10% of the width...
divider = make_axes_locatable(ax)
legend_ax = divider.append_axes('right', '10%')
legend_ax.axis('off')

# Now make the legend on the new axis
legend_ax.legend(bars, names, mode='expand', frameon=False)

plt.show()


Hope that helps!
-Joe
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Re: [Matplotlib-users] Setting y-tick locations on an image plot changes y-axis limits, Bug or expected behavior?

[Joe Kington]

Thanks for the amazingly quick turnaround!
-Joe

On Sat, Aug 21, 2010 at 1:34 PM, Eric Firing efir...@hawaii.edu wrote:

 On 08/20/2010 12:18 PM, Joe Kington wrote:
  Hi,
 
  I've recently noticed that setting the y-tick locations on an image plot
  changes the y-axis limits, while changing the x-tick locations does not
  change the x-axis limits.  I wouldn't have expected either to change the
  axis limits, but it seems quite inconsistent that the y-axis and x-axis
  behave differently in this situation.
 
  The axis limits are easily restored by a call to ax.axis('image'), but
  this seems unnecessary.
 
  A very quick search of the bug tracker doesn't reveal anything matching
  this description...
 
  Is this expected behavior or a bug?
 
  As an example, setting the y-tick locations changes the y-axis limits:
  import numpy as np
  import matplotlib.pyplot as plt
 
  num = 10
  data = np.arange(num**2).reshape((num,num))
 
  fig = plt.figure()
  ax = fig.add_subplot(111)
 
  cax = ax.imshow(data)
 
  ax.set_yticks(range(num//2))
 
  fig.show()
 
  However, setting the x-tick locations does not change the axis limits...
  import numpy as np
  import matplotlib.pyplot as plt
 
  num = 10
  data = np.arange(num**2).reshape((num,num))
 
  fig = plt.figure()
  ax = fig.add_subplot(111)
 
  cax = ax.imshow(data)
 
  ax.set_xticks(range(num//2))
 
  fig.show()
 
  As a reference, I'm running matplotlib 1.0.0 with python 2.7.0 on a
  64-bit Linux (kernel 2.6.31) machine.  I can confirm this on python 2.6
  and matplotlib 0.99 as well, though.
 
  If this isn't expected behavior, I'll be glad to open a bug report.

 Joe,

 Thanks for the report.  The fix is in svn 8652 and 8653.

 Eric

 
  Thanks!
  -Joe


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[Matplotlib-users] Setting y-tick locations on an image plot changes y-axis limits, Bug or expected behavior?

[Joe Kington]

Hi,

I've recently noticed that setting the y-tick locations on an image plot
changes the y-axis limits, while changing the x-tick locations does not
change the x-axis limits.  I wouldn't have expected either to change the
axis limits, but it seems quite inconsistent that the y-axis and x-axis
behave differently in this situation.

The axis limits are easily restored by a call to ax.axis('image'), but
this seems unnecessary.

A very quick search of the bug tracker doesn't reveal anything matching this
description...

Is this expected behavior or a bug?

As an example, setting the y-tick locations changes the y-axis limits:
import numpy as np
import matplotlib.pyplot as plt

num = 10
data = np.arange(num**2).reshape((num,num))

fig = plt.figure()
ax = fig.add_subplot(111)

cax = ax.imshow(data)

ax.set_yticks(range(num//2))

fig.show()

However, setting the x-tick locations does not change the axis limits...
import numpy as np
import matplotlib.pyplot as plt

num = 10
data = np.arange(num**2).reshape((num,num))

fig = plt.figure()
ax = fig.add_subplot(111)

cax = ax.imshow(data)

ax.set_xticks(range(num//2))

fig.show()

As a reference, I'm running matplotlib 1.0.0 with python 2.7.0 on a 64-bit
Linux (kernel 2.6.31) machine.  I can confirm this on python 2.6 and
matplotlib 0.99 as well, though.

If this isn't expected behavior, I'll be glad to open a bug report.

Thanks!
-Joe
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Re: [Matplotlib-users] Fit a plane to a set of xyz points

[Joe Kington]

On Tue, Jul 27, 2010 at 1:37 PM, Friedrich Romstedt 
friedrichromst...@gmail.com wrote:

 2010/7/26 Mathew Yeates mat.yea...@gmail.com:
  Is there a simple function call for this? And finding the distance of
  a point to the plane?

 Hmm, when you are interested in the z distance alone, it should be a
 matrix equation:

 Z = X * m_x + Y * m_y + 1 * n

 Meaning you can invert it with Moore-Penrose pseudoinversion, i.e.,
 numpy.lstsq()?

 When you have weights on Z, normalise first.

 Friedrich


Just one quick note on this:

If you fit Z = aX + bY + c, you won't be able to resolve vertical planes.
 Likewise, if you fit x = aY + Bz + c or y = aX + bZ + c you won't be able
to resolve horizontal planes.

If you need to robustly fit a plane to a point cloud, you'll need to try all
three formulations. See here for a quick example of what Friedrich mentioned
using all three formulations and choosing the most robust result:
http://code.google.com/p/python-geoprobe/source/browse/geoprobe/common.py#198

As far as finding the distance of a given point (x0, y0, z0) to the plane
defined by 0 = ax + by + cz + d, the equation is just abs(a * x0 + b * y0
+ c * z0 + d) / sqrt(a**2 + b**2 + c**2).  See
herehttp://mathworld.wolfram.com/Point-PlaneDistance.html for
a more detailed explanation.

-Joe
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Re: [Matplotlib-users] Smooth contourplots

[Joe Kington]

It sounds like you're wanting a gaussian kernel density estimate (KDE) (not
the desktop!).  The other options you mentioned are for interpolation, and
are not at all what you're wanting to do.

You can use 
scipy.stats.kde.gaussian_kde()http://www.scipy.org/doc/api_docs/SciPy.stats.kde.gaussian_kde.html.
However, it currently doesn't take a weights array, so you'll need to modify
it for your use case.

If you prefer, I have faster version of a gaussian KDE that can take a
weights array.  It's actually slower than the scipy's gaussian kde for a low
number of points, but for hundreds, thousands, or millions of points, it's
several orders of magnitude faster.  (Though the speedup depends on the
covariance of the points... higher covariance = slower, generally speaking)

Here's a quick pastebin of the code.  http://pastebin.com/LNdYCZgw

To use it, you do something like the below... (assuming the code in the
pastebin is saved in a file called fast_kde.py)

import numpy as np
import matplotlib.pyplot as plt
from fast_kde import fast_kde

# From your description of your data...
weights, x, y = np.loadtxt('chain.txt', usecols=(0,4,6)).T

kde_grid = fast_kde(x, y, gridsize=(200,200), weights=weights)

# Plot the grid
plt.figure()
plt.imshow(kde_grid, extent=(x.min(), x.max(), y.max(), y.min())

# Reverse the y-axis
plt.gca().invert_yaxis()

plt.show()

Hope that helps a bit,
-Joe




On Sat, Jul 24, 2010 at 3:56 AM, montefra franz.berges...@googlemail.comwrote:


 Hi,

 I am writing a program that reads three columns (one column containing the
 weights, the other two containing the values I want to plot) from a file
 containing the results from a MonteCarlo Markov Chain. The file contains
 thousends of lines. Then create the 2D histogram and make contourplots.
 Here
 is a sample of the code (I don't know if is correct, it's just to show what
 I do)

  import numpy as np
  import matplotlib.pyplot as mplp
  chain = np.loadtxt(chain.txt, usecols=[0,4,6])   #read columns 0 (the
  weights), 4 and 6 (the data), from the file chain.txt
  h2D, xe, ye = np.histogram2D(chain[:,1],chain[:,2], weights=chain[:,0])
  #create the 2D histogram
  x = (xe[:-1] + xe[1:])/2. #x and y values for the plot (I use the mean
  of each bin)
  y = (ye[:-1] + ye[1:])/2.
  mplp.figure()   #open the figure
  mplp.contourf(x, y, h2D.T, origin='lower')  #contour plot

 As it is the contours are not smooth and they look not that nice. After
 days
 of searches I've found three methods and tried, unsuccesfully, to apply
 them
 1) 2d interpolation: I got segmentation fault (on a quadcore machine with
 8Gb of RAM)
 2) Rbf (radial basis functions): I got wrong contours
 3) ndimage: it creates spurious features (like secondary peaks parallel to
 the direction of the main one)

 Before beginning with Python, I used to use IDL to plot, and there is a
 function 'smooth' that smooth for you 2D histograms. I haven't found
 anything similar for Python.
 Does anyone have an idea or suggestion on how to do it?

 Thank in advance
 Francesco

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Re: [Matplotlib-users] extract country borders data convert to shapefile

[Joe Kington]

If you just need the GSHHS data in shapefile format, it's available on the NOAA
GSHHS website http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html.

If you prefer a direct link [96MB zip file]:
http://www.ngdc.noaa.gov/mgg/shorelines/data/gshhs/version2.0/shapefiles/gshhs_shp_2.0.zip

Hope that helps,
-Joe


On Wed, Jun 30, 2010 at 6:34 PM, P. R.M. romero...@hotmail.com wrote:

  I have a non-matplotlib related project that requires usage of GSHHS
 dataset shapefiles.
 The regular GSHHS dataset doesnt appear to include political boundaries,
 however the GSHHS dataset used by matplotlib/basemap does include
 country/border data. I'd like to extract matplotlib/basemap GSHHS country
 data  convert it to shapefile format.

 specifically, the following files that are included with
 matplotlib/basemap:
 countries_c.dat
 countries_f.dat
 countries_h.dat
 countries_i.dat
 countries_l.dat
 countriesmeta_c.dat
 countriesmeta_f.dat
 countriesmeta_h.dat
 countriesmeta_i.dat
 countriesmeta_l.dat

 how can I convert these files to shapefile format, or where can I get
 shapefiles that already include this data  that are based on the GSHHS
 coastline data?

 please help,
 thanks,
 P.Romero



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Re: [Matplotlib-users] extract country borders data convert to shapefile

[Joe Kington]

Ah, sorry I misunderstood.

You can get them by using GMT's gshhs tool (or just using pscoast with the
right options) to dump out the political borders in GMT format and then
convert them to a shapefile using ogr2org (or whichever tool you find
easiest... Personally I use the python wrappers around ogr for more
control.)

To save you a bit of trouble, I zipped political boundary shapefiles (from
GSHHS version 2.0, so they'll coincide perfectly with the GSHHS coastline
data) and put them here:
http://www.geology.wisc.edu/~jkington/borders.ziphttp://www.geology.wisc.edu/%7Ejkington/borders.zip

There's probably a way to do it using basemap, as well, but I don't know it
off the top of my head.

Hope that helps,
-Joe


On Wed, Jun 30, 2010 at 9:33 PM, P. R.M. romero...@hotmail.com wrote:

  hi,
 thanks for the response.

 I already have  know how to use the GSHHS coastline data; what I need is
 political boundaries (country borders) data that will coincide with the
 GSHHS coastline data. In other words, I can't simply use any political
 boundaries shapefile, since the coastlines will not line up with the various
 resolutions of the GSHHS coastline data. I hope this makes it a little more
 clear...

 So, I'd like to source the country border data from matplotlib/basemap's
 GSHHS dataset, and convert it to shapefile format.
 can anyone please provide some guidance on how to achieve this?

 thanks again,
 P.Romero

 --
 Date: Wed, 30 Jun 2010 19:43:34 -0500
 From: jking...@wisc.edu
 Subject: Re: [Matplotlib-users] extract country borders data  convert to
 shapefile
 To: romero...@hotmail.com
 CC: matplotlib-users@lists.sourceforge.net


 If you just need the GSHHS data in shapefile format, it's available on the 
 NOAA
 GSHHS website http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html.

 If you prefer a direct link [96MB zip file]:

 http://www.ngdc.noaa.gov/mgg/shorelines/data/gshhs/version2.0/shapefiles/gshhs_shp_2.0.zip

 Hope that helps,
 -Joe


 On Wed, Jun 30, 2010 at 6:34 PM, P. R.M. romero...@hotmail.com wrote:

  I have a non-matplotlib related project that requires usage of GSHHS
 dataset shapefiles.
 The regular GSHHS dataset doesnt appear to include political boundaries,
 however the GSHHS dataset used by matplotlib/basemap does include
 country/border data. I'd like to extract matplotlib/basemap GSHHS country
 data  convert it to shapefile format.

 specifically, the following files that are included with
 matplotlib/basemap:
 countries_c.dat
 countries_f.dat
 countries_h.dat
 countries_i.dat
 countries_l.dat
 countriesmeta_c.dat
 countriesmeta_f.dat
 countriesmeta_h.dat
 countriesmeta_i.dat
 countriesmeta_l.dat

 how can I convert these files to shapefile format, or where can I get
 shapefiles that already include this data  that are based on the GSHHS
 coastline data?

 please help,
 thanks,
 P.Romero



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Re: [Matplotlib-users] Reverse y-axis?

[Joe Kington]

There's no single function to do it, but just reversing the current ylimits
manually is a one-liner.  (plt is matplotlib.pyplot, here)

plt.ylim(plt.ylim()[::-1])

Hope that helps,
-Joe


On Fri, Jun 25, 2010 at 6:41 AM, Preben Randhol rand...@pvv.org wrote:

 Hi

 I need to plot some data vs depth. In stead of giving depths as
 0,-1000,-2000, I want to give the depths as 0,1000,2000 but when plotting
 the data goes downwards (as if they had been negative). Can I get the
 y-axis to reverse so it goes downwards and not upwards?





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Re: [Matplotlib-users] matplotlib and large array

[Joe Kington]

One workaround is to try converting it to a PIL image first.

For whatever reason, imshow chokes on large arrays, but handles PIL images
of the same array just fine.

However, you'll need to set the colormap and such through PIL, which can be
a bit frustrating if you're unfamiliar with it.  Also, you'll quite likely
need to rescale your data if it's a float.

Hope that points you in the right direction, anyway
-Joe

On Wed, Jun 9, 2010 at 1:31 PM, Massimo Di Stefano massimodisa...@gmail.com
 wrote:

 Hi All,

 i need to work with a relative large images 60 mb (single band geotiff
 file)
 i store it in python as a numpy array using python-gdal,
 the array dinension is (7173 X 7924) single band image,
 but tring to display it with matshow/imageshow
 or other matplotlib functions i have that python freeze itself and is not
 able to load the image.

 if i use a subset of the image, i 'm able to display it
 or at least i hade to reduce its resolution using hacks like :

 reduced_array = array[::3,::3]

 i don't need full resolution dataset when the image is displaied with a
 full zoom out
 so the reduction  reduced_array = array[::3,::3]   is good to show the
 complete image
 but when i zoom in the image i obviously lost data (less resolution)

 what do you use to display large dataset ?

 i'm thinking  about a piramid with multy array based on the different
 zoom levels
 .. but maybe this idea is not so cool.
 someone already has developed similar code ?

 thanks to All for any suggestion!

 Regards,

 Massimo

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Re: [Matplotlib-users] Plotting a vector in matplotlib

[Joe Kington]

Isn't that what
quiverhttp://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.quiverdoes?
Or am I misunderstanding the question?

2010/5/3 aditya bhargava bluemangrou...@gmail.com

 Thanks Johan and Matthias,
 I was just wondering if there was a built-in way to do this in matplotlib.
 It seems like it would be a useful method to have.

 Adit


 2010/5/3 Johan Grönqvist johan.gronqv...@gmail.com

 2010-05-02 20:19, aditya bhargava skrev:
  Is there a straightforward way of plotting a vector in matplotlib?
  Suppose I want to plot the vector [1 2]'. If I pass this vector in to
  plot(), I get the line that passes through (0,1), (1,2). Instead I want
  the line that passes through (0,0),(1,2).
 

 I use pyplot.Arrow to visualize displacement fields.

 This is a snippet copied from the code I use (it sits in a loop over all
 vectors I want to plot):


 arr = plt.Arrow(x, y, dx, dy)
 plt.gca().add_patch(arr)

 In your case, you would have (x, y) = (0, 0) and (dx, dy) = (1, 2).

 Regards

 Johan



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